Tracking economic activity in response to the COVID-19 crisis using nighttime lights – The case of Morocco
Bibliographic record
Abstract
Over the past decade, nighttime lights have become a widely used proxy for measuring economic activity. This paper examines the potential for high frequency nighttime lights data to provide "near real-time" tracking of the economic impacts of the COVID-19 crisis in Morocco. At the national level, there exists a statistically significant correlation between quarterly movements in Morocco's overall nighttime light intensity and movements in its real GDP. This finding supports the cautious use of lights data to track the economic impacts of the COVID-19 crisis at higher temporal frequencies and at the subnational and city levels, for which GDP data are unavailable. Relative to its pre-COVID-19 trend growth path of lights, Morocco experienced a large drop in the overall intensity of its lights in March 2020 following the country's first COVID-19 case and the introduction of strict lockdown measures, from which it has subsequently struggled to recover. At the subnational and city levels, while all regions and cities examined shared in March's national decline in nighttime light intensity, some suffered much larger declines than others. Since then, the relative effects of the COVID-19 shock across regions and cities appear to have largely persisted. Notwithstanding these findings, however, further research is required to ascertain the exact causes of the observed changes in light intensity and to fully verify that the results are driven by anthropogenic causes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".