Bibliographic record
Abstract
The purpose of this paper is to examine alternative ways of raising government revenue in Alberta. There are several ways to approach this question. One would be to adopt a revenue neutral perspective, which would focus on the design and configuration of the tax system without a view to raising more revenue; a so-called revenue neutral perspective. Another would be to evaluate various options from the perspective of raising sufficient revenue to eliminate or reduce the deficit going forward, or more generally to close the fiscal gap, in order to put the government on a sustainable fiscal path. Neither of these approaches is followed here. While the first approach is in some ways more pure from a tax policy perspective it is possible that at least a modest increase in revenues will be required to put the government on a sustainable fiscal path. Moreover, there are several moving parts to the revenue system, and it is very difficult in this context to accurately take account of the various interactions. In terms of the second approach, it is not clear precisely how much higher revenues should be relied upon versus lower spending to restore fiscal sustainability. As Trevor Tombe puts it in a recent analysis of the sustainability of Alberta’s fiscal policy, “the province has neither a revenue problem nor a spending problem; it has a budget problem.”[1] This problem can be addressed from the revenue side, the expenditure side or a combination of both. I don’t take a strong position on this. Rather I examine some of the key sources of government revenue and evaluate various policy options that would alter the tax mix without imposing a hard constraint of revenue neutrality or the need to generate a given amount of revenue to close the fiscal gap. In some cases I argue for tax reductions and in others for tax increases. The focus is on “first principles”, drawing on what economic research on tax policy has to say about implementing an efficient and equitable tax system within an Alberta context. Of course, as will be discussed, this inevitably involves trade-offs and, also inevitably, some disagreement on the nature of those trade-offs. The net impact on total revenue from the type of changes I discuss in what follows may be positive or negative, but the magnitude of the revenue gains can be scaled up or down by adjusting rates appropriately; the first-principles laid out here provide guidance on how best to go about that. Based on my analysis, and as a preview of my conclusions, I argue the following: Cut the corporate income tax rate as planned. Keep the full provincial carbon tax. Maintain the progressive rate structure in the personal income tax (no flat tax), but consider a “middle class” personal income tax cut. Impose a harmonized provincial sales tax. While it is not the focus of the paper, I also argue for reintroducing systematic contributions of resource revenue to a stabilization fund, and eventually the Heritage Savings Trust Fund. [1] Tombe (2018).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".