Short‐term impact of COVID‐19 on consumption spending and its underlying mechanisms: Evidence from Singapore
Bibliographic record
Abstract
We examine the short-term impact of COVID-19 on consumption spending and its underlying mechanisms using individual-level monthly panel data from Singapore. Although Singapore's case fatality rate was one of the lowest in the world in the early stage of the pandemic (0.05%), we find that the COVID-19 pandemic reduced household consumption spending by almost one quarter at its peak, with a larger response from households with above-median wealth. We show that the reduction in consumption spending is associated with the nationwide lockdown policy, heightened economic uncertainty and reduced income. In addition, we find a substantial increase in monthly savings among households without income losses, suggesting a substantial rebound in consumption spending after the lifting of the lockdown. The results from June 2020 confirm this conjecture, as we find that consumption spending rebounded by about 10 percentage points in that month.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".