Trends, Peaks, and Troughs: National and Regional Employment Cycles in Canada
Bibliographic record
Abstract
When the 2008 global recession hit Canada and national unemployment rates began to rise, the federal government soon found itself under pressure to do something to help. That something ended up being the Economic Action Plan, a multi-billion-dollar spending initiative spread across the country. But, in at least one part of the country, no help was really needed at all. In New Brunswick, the recession had barely any impact on the state of the job market. Federal spending there would have been at best, unnecessary or, at worst, harmful, crowding out private investment while having an inflationary effect on the regional economy. Unfortunately, any informed assessment of how much stimulus is required in each province requires many months of data. Governments, impatient to show they are taking action, hastily create plans that may cause them to spend needlessly — or even harmfully — in some provinces while possibly coming up short in provinces facing the deepest economic crisis. In this report we show this problem is not unique to the most recent recession. Our analysis uses monthly data on labour market conditions to show that the timing and depth of employment recessions varies widely across provinces and that this has been so in every recession since 1976. These results suggest that, to be effective, spending “action plans” and tax cuts must be targeted to benefit only certain regions of the country at certain times. The time it takes to formulate such targeted responses, and the obvious political challenges they involve, beggars the question of whether a national strategy can ever be designed that takes into account the varying economic conditions of a country with such a wide variety of industries and economic variables. Examinations of the role played by governments in alleviating the recent recessions in Canada, the United States and elsewhere have emphasized the importance of relying instead on “automatic stabilizers” — pre-established mechanisms such as employment insurance, social assistance, and prescribed tax adjustments — to alleviate the impact of recessions. Before the next recession comes, the federal government — and provincial governments as well — would be wiser to prepare by investing resources in legislating well-designed automatic stabilizers so these processes are in place to naturally kick in precisely where and when they are needed. With the right formula of automatic stabilizers responding quickly and precisely to economic contractions, the main job left for politicians would be persuading the public that resorting to action plans and national strategies is something we are better off avoiding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".