Panel data-set of the paper Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks
Bibliographic record
Abstract
Data-set of the paper Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks for replication purpose of the Data Editor of AEJMacro. Detailed information on the data-set is in the readme file in the public repository openicpsr-175241 (under review). We study the dynamic interaction between Covid-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for 1-2 months. These shocks are the main drivers of the pandemic, explaining between 20-60% of the average and historical variability in mobility, cases, and deaths worldwide. The policy tradeoff associated to non-pharmaceutical interventions is 1pp less economic mobility per day for 8% fewer deaths after three months. The panel data-set contains the main data to perform the analysis in the paper. It contains dailiy data for (in sheets) Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Saudi Arabia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom and United States. Included variables are: Confirmed Cases, Total Deaths, Days Last Reported Case, Total Tests, School Closing, Workplace Closing, Cancel Public Events, Restrictions Gatherings, Close Public Transport, Stay at Home Requirements, Restrictions Internal Movement, International Travel Controls, Income Support, Debt/Contract Relief, Fiscal Measures, International Support, Public Information Campaigns, Testing Policy, Contact Tracing, Healthcare Emergency Investment, Investment Vaccines, Stringency Index, Small Cap, Large Cap, Government Benchmarks 3 Month, Government Benchmarks 1 Year, Government Benchmarks 2 Year, Government Benchmarks 5 Year, Government Benchmarks 10 Year, FX Indices Broad, FX Indices Narrow, Mobility Retail Mobility Grocery, Mobility Parks, Mobility Transit Stations Mobility Workplaces, Mobility Residential. Period: 30.12.2016 to 31.08.2020. All data are downloaded from Macrobond.
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 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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.180 | 0.073 |
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".