MétaCan
Menu
Back to cohort
Record W2219534689 · doi:10.55016/ojs/sppp.v8i1.42524

Sources of Debt Accumulation in Resource-Dependent Provinces

2015· article· en· W2219534689 on OpenAlexaffabout
Ronald D. Kneebone

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDebtResource (disambiguation)BusinessGeographyNatural resource economicsEconomic geographyEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Governments in provinces relying on natural resource commodities for significant amounts of revenue face the distinct challenge of unpredictably fluctuating budget circumstances. As politicians routinely point out, much of that challenge is in the volatility of global commodity prices. But a big part of it is actually the policies of the governments themselves. In fact, when effects of commodity prices, economic cycles and fiscal policy are separated from one another, one of the biggest impacts on government debt over the last 30 years in Canada’s four resource-dependent provinces — Alberta, Saskatchewan, British Columbia and Newfoundland and Labrador — has been government policy. While years of booming economies have offset years of busts, virtually all the debt racked up by these provinces over more than three decades has been a combination of movements in commodity prices and political decisions. In Alberta, over three periods since the early 1980s, totalling more than 15 years cumulatively, it was policy — not energy prices or economic factors — that had the biggest impact on government debt levels. From 1988–89 to 1993–94, Progressive Conservative policies were the biggest factor in raising Alberta’s debt, and from 1995–96 to 1999–2000, the Klein government’s policies were the biggest factor in reducing Alberta’s debt. The policies of then premier Ralph Klein also played the biggest role in reducing debt from 2001–02 to 2003–04, while from 2006–07 to 2013–14, the policies of the Stelmach and Redford governments outweighed economic and commodity-price effects in ways that both reduced debt at times, and then raised it again. Over the entire period from 1982–84 to 2013–14, PC government policy increased Alberta’s debt ratio by 9.5 percentage points of GDP, while the business cycle decreased it by only one percentage point, and the commodity-price cycle decreased it by only 1.9 points. In Saskatchewan, the policy component raised the provincial debt ratio by 11.6 percentage points of provincial GDP from 1982–84 to 2013–14. The business cycle added 1.5 points and the commodity-price cycle decreased the debt ratio by 6.1 points. Ironically, given assumptions about party proclivities, it was Progressive Conservative government policies that added most of that debt, and NDP government policies that made the most progress in reducing it. In Newfoundland and Labrador, where a reliance on resource revenue is a more recent phenomenon, the policies of both PC and Liberal governments were almost indistinguishable, together reducing the debt ratio by 9.8 percentage points of GDP from 1982–84 to 2013–14, while the effect of commodity prices reduced it by 16.9 percentage points. But in B.C., government policy was, as in the other western provinces, the biggest factor on the debt ratio: decreasing it by 12.5 percentage points of GDP, compared to the increase of two points caused by economic cycles, and the reduction of 4.9 percentage points caused by commodity prices. Whatever the politicians in resource-dependent provinces say about their unpredictable budgeting challenges, clearly policy can have the biggest impact on debt accumulation. As it happens, that is also the one factor over which those politicians actually have total control. †

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)0.001
metaresearch head score (Gemma)0.006
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 score0.897
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.114
GPT teacher head0.306
Teacher spread0.192 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueThe School of Public Policy PublicationsSame topicFiscal Policies and Political EconomyFrench-language works237,207