The relationship between pandemic containment measures, mobility and economic activity
Bibliographic record
Abstract
This paper first constructs a regional-scale indicator that seeks to gauge the volume of measures implemented at each point in time to contain the pandemic. Using textual analysis techniques, we analyse the information in press news. At the start of the pandemic, measures were taken in a centralised fashion; but from June, regional differences began to be seen and increased in the final stretch of the year. Second, using linear estimates, with monthly data and a level of regional disaggregation, the paper documents how most of the reduction in mobility observed in Spain has been due to the restrictions imposed. However, there has been a perceptible change in this relationship over recent months. In the early stages of the pandemic, the reduction in mobility was found to be greater than would be inferred by the restrictions approved. That is to say, at the outset there was apparently some voluntary reduction in mobility. Yet following lockdown-easing, the behaviour of mobility has fitted more closely with what might be attributed to the containment measures in force. Finally, the findings in the paper suggest that most of the decline in economic activity since the start of the crisis can be explained by the reductions observed in mobility. The analysis considers only the short-term effects on activity, which is very useful for preparing the projections on GDP behaviour in the current quarter. Conversely, the methodological approach pursued does not allow for evaluation of the effect of the pandemic containment measures on activity over longer time horizons. In particular, the adverse impact on the economy’s output that occurs concurrently as a result of the restrictions may be countered in the medium term by an effect of the opposite sign, to the extent that the restrictions imposed today may serve to prevent other more forceful ones in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".