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Record W2899862586 · doi:10.55016/ojs/sppp.v11i1.43432

Alberta’s Fiscal Responses to Fluctuations in Non-Renewable Resource Revenue

2018· article· en· W2899862586 on OpenAlexaffabout
Ergete Ferede

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsMacEwan University
Fundersnot available
KeywordsRevenueResource (disambiguation)Natural resource economicsRenewable energyRenewable resourceEconomicsEnvironmental scienceBusinessEnvironmental economicsFinanceComputer scienceEngineering

Abstract

fetched live from OpenAlex

We investigate how successive Alberta governments have responded to shocks in non-renewable resource revenue over the period 1970 to 2017. Our results show that Alberta governments have increased spending by 63 cents in the fiscal year following a one dollar increase in real per capita non-renewable resource revenues. On the other hand, when non-renewable resource revenues have declined year over year, Alberta governments have not adjusted spending or other own source tax revenues. As a result of these asymmetric responses to fluctuations in resource revenues, the province’s stock of financial assets has declined and its net debt has increased by $10,834 per capita or in total $46 billion dollars. The policy implication of our results is that provincial governments should put increases in non-renewable resource revenues in a fiscal stabilization fund or Alberta Heritage Saving Trust Fund rather than spending two-thirds of any short-term increase in revenues. This would result in a less volatile spending pattern and a sustainable fiscal policy with better services and lower tax rates.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.076
GPT teacher head0.286
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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