Essays on the Impacts of Oil Price Fluctuations on the Canadian Economy
Bibliographic record
Abstract
This dissertation examines the effects of oil price fluctuations on the Canadian economy at the provincial and sectoral levels, accounting for the heterogeneities in economic activities between two groups of net oil-exporting and net oil-importing Canadian provinces and across different sectors. The first essay employs a nonparametric panel data technique to investigate the impacts of oil price fluctuations on the GDP per capita in two groups of net oil-exporting and net oil-importing provinces in Canada over the period 1984-2016. The results show that the relationship between oil price and GDP per capita is time-varying in both groups of Canadian provinces. While both groups of provinces benefited from the striking hike in oil prices during the 2000s, the relatively low oil prices before 2000 had positive effects only in net oil-importing provinces. Furthermore, the findings highlight the vital role of interprovincial trade in mitigating the adverse effects of oil price volatility on Canadian provinces. The second essay analyzes the relationship between oil price changes and economic growth in Canadian provinces using the quantile-on-quantile (QQ) approach. The results show that the impacts of oil price changes on Canadian provinces' economic growth are considerably heterogeneous across different quantiles of the two variables. The results also reveal that the oil-economic growth relationship is heterogeneous across Canadian provinces, both within each block of net oil-importing and net oil-exporting provinces and between the two blocks. In the last essay, I develop a two-sector dynamic stochastic general equilibrium (DSGE) model, incorporating both oil-producing and oil-dependent final goods sectors, to investigate the heterogeneous impacts of oil price shocks on these two sectors. The model's findings reveal that a positive oil price shock has sizable positive effects on the oil-producing sector and shifts the labour force from the final goods sector to the booming oil sector. Furthermore, the results show that the positive oil price shock initially hurts the final goods sector. However, the adverse effects of higher oil prices on this sector are reversed after a relatively short period due to capital accumulation resulting from the positive wealth effects of higher oil prices for Canadian households.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".