Financing Local Government and Development in Canada in the Aftermath of a Global Pandemic: Continuity and Change
Bibliographic record
Abstract
In the post-pandemic environment, local governments must confront a challenging fiscal environment. Drawing on insights provided over the years by Richard Bird, the author re-examines certain pervasive themes found in discussions and analysis of municipal finance, such as the merits of benefits-based taxation at the local level, the hierarchical constraints on municipal fiscal decision making, and the reality of a perpetual fiscal crisis at the local level. The underlying issue of having to finance a broad set of expenditures on public services from a narrow revenue base has resulted in intergovernmental tensions and continuing debate over the capacity of the property tax base to meet the demands placed on it. Financing local government can be described in terms of borrowing, spending, and taxing. Each of these areas of municipal fiscal decision making was affected by the COVID-19 pandemic. Borrowing rules remained hierarchically constrained, pressure to increase expenditures grew, and tax and fee-based revenues were adversely affected. Further, it seems likely that the municipal fiscal base will be narrowed given the controversy around development charges. The author uses data from Ontario to illustrate the impact of the pandemic on municipal fiscal health. In addition, the paper includes the Richard Bird Urban Fiscal Health Dashboard to illustrate aspects of the long-run fiscal health of Ontario's local governments. Throughout the paper, the author raises questions that merit further research informed by the perspective that Richard Bird brought to our understanding of the mechanics and implications of local fiscal decision making.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".