COVID-19, Containment and Consumption
Bibliographic record
Abstract
We assess the impact of the COVID-19 pandemic on consumption indicators by estimating the effects of government-mandated containment measures and of the willingness of individuals to voluntarily physically distance to prevent contagion. To do this, we use weekly panel regressions across Canadian provinces to study how differences in both containment measures and voluntary physical distancing affect consumption, proxied by transaction data. We also conduct a similar panel analysis across 28 advanced economies using retail mobility data as a proxy for in-person consumption of goods and services. Two main findings are broadly robust across a variety of tests and specifications. First, indicators of both government containment measures and voluntary physical distancing are negatively correlated with consumption indicators, with the latter relationship showing variation over time. Second, contact-intensive and other highly restricted sectors in Canada were generally more affected by increases in the stringency of government containment measures and voluntary physical distancing. In contrast, the impact from voluntary physical distancing on spending categories deemed essential by some Canadian provincial governments was muted relative to the impact on other categories.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".