How much does mobility matter for value-added tax revenue? Cross-country evidence around COVID-19
Bibliographic record
Abstract
Abstract Countries need to evaluate both costs and benefits when mandating mobility restrictions for their population. This paper studies one edge of this debate. Namely, to what extent mobility reductions and confinement measures impact Value Added Tax (VAT) collection, which is an increasingly important type of fiscal revenue. Using evidence across twenty nations and over time, we measure these effects around the COVID-19 Pandemic. For that, we benefit from the novel IDB-CIAT monthly dataset on aggregate VAT revenues (2019-2020), combining it with both mobility-restriction policies and mobility outcomes. On average, monthly VAT revenues fell up to 30% around the event of largest drop in mobility for each country. We also estimate mobility elasticities of VAT revenue. Mobility-restriction policies rising by 10% were associated with drops in VAT of 1.4%; while a 10% rise in actual mobility decreased VAT revenues by 3%. Furthermore, we show both elasticities were significantly smaller in the last quarter of 2020. Beyond the pandemic, results matter as a benchmark for macroeconomic variables under large disruptions. JEL Classification: H20, H84, E62.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".