Payment Habits during Covid-19: Evidence from High-Frequency Transaction Data
Bibliographic record
Abstract
Abstract The Covid-19 pandemic, in Canada and around the world, accelerated trends toward non-cash payments. In this paper, we assess the impact of high-frequency fluctuations in the severity of the pandemic on payment behaviour. These fluctuations are measured by daily changes in restrictions (“stringency”) and reported case counts. Our main measures of payment habits are the ratios of the value and transaction counts of cash withdrawals to debit card payments. Econometrically, we use local projections to estimate the effects on payment habits. We find evidence that consumer behaviour adjusted during the pandemic: consumers withdrew less cash relative to card payment and avoided frequent trips for cash withdrawals and point-of-sale purchases through higher transaction amounts. Based on our modelling, once stringency measures eased and case counts receded, cash use partially recovered, although not to pre-pandemic levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".