Accrual Versus Cash Basis of Accounting in the Canadian <scp>COVID</scp>‐19 Subsidy Programs*
Bibliographic record
Abstract
ABSTRACT The principal eligibility criterion for the Canada Emergency Wage Subsidy (CEWS) during the COVID‐19 pandemic was a reduction of at least a prespecified percentage in monthly revenues compared to the same months in the prior year, or compared to January and February 2020 (just prior to the launch of CEWS in March 2020). The revenues could be measured using accrual or the cash basis of accounting. Since subsidy applicants could choose the cash method of accounting and use January and February 2020 as reference periods, seasonal businesses that generated most of their revenues in January and February could claim subsidies without experiencing any reductions in revenue. We illustrate how a seasonal business with higher monthly accrued revenues compared to the pre‐pandemic year could be eligible for CEWS by using the cash basis of accounting in the subsidy application even though it would not qualify using the accrual accounting method. It seems inequitable for business or wage subsidies to be based on the choice of accounting methods. There is no sound public policy reason to subsidize (or tax) one firm more than another just because they use a different method of accounting. Evaluating accounting methods embedded within government subsidy programs is an important endeavor to ensure neutrality and effectiveness of public spending programs.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".