Viewing the COVID-19 Pandemic from Space: The Effect of COVID-related Mobility Declines on Night Light Brightness in Canada
Bibliographic record
Abstract
High-frequency economic data for small areas is often difficult to obtain in Canada and other countries. This paper overcomes this limitation by using monthly data derived from satellite night light images as a proxy for economic activity in Canadian Census Divisions. This proxy is used in conjunction with Facebook mobility data to estimate the effects of mobility declines due to COVID-19 on economic activity. I find robust evidence that reductions in movements are strongly negatively associated with declines in luminosity. Further analyses suggest that this effect is weaker in more densely populated areas, but stronger in Census Divisions with a higher concentration of retail businesses. My findings suggest that policies which reduce the need for in-person activities can mitigate the negative effects of COVID-related mobility reductions on economic activity. This paper also further highlights the value of using monthly satellite night lights data in economic analysis.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".