Cash and COVID-19: The impact of the second wave in Canada
Bibliographic record
Abstract
"We use consumer surveys conducted in April, July and November 2020 to study how the COVID-19 pandemic affected the demand for cash and the use of various methods of payment. Continuing from Chen et al. (2020, 2021), we use data from the Bank Note Distribution System (BNDS) to track how the amount of cash in circulation changed throughout 2020. The November 2020 survey included a three-day payment diary. We compare this diary with similar diaries from 2009, 2013 and 2017 to study long-term trends in cash use and payment methods. Our key findings are as follows: - Cash in circulation increased from $83 billion before the pandemic to more than $100 billion by the end of 2020. We find that the large-denomination bank notes ($50 and $100) contributed the most of this increase over this period. - Canadians’ cash-on-hand holdings in November (median $80) were comparable to levels seen in the first wave of the pandemic in April 2020 (median $85). Use of all payment methods (measured by the percentages of Canadians using them) was stable or increasing in November compared with July. Cash use saw the largest increase, with 59 percent of Canadians reporting that they used cash for transactions in November, up from 54 percent in July. - A large majority of Canadians (80 percent) in the November survey reported that they have no plans to go cashless in the next five years. This is similar to findings obtained in July (78 percent). - The shares of the volume (number) and value of transactions paid with cash declined between November 2017 and November 2020. At the same time, shares of contactless payments increased significantly. Nevertheless, cash continues to be used for a significant portion of low-value transactions. More specifically, in - - November 2020, cash was used for 40 percent of the volume of low-value transactions (below $15). - Cash use tends to be more prevalent in certain demographic groups. Older, less-educated and low-income individuals use cash for more of their transactions than other Canadians do."
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".