The Marginal Propensity to Consume of 2020 COVID-19 Stimulus Payments in Peru
Bibliographic record
Abstract
The COVID-19 pandemic has led to unprecedented economic challenges across the world. To combat these challenges, the government of Peru used fiscal stimulus in the form of direct subsidies paid to vulnerable populations for social protection and to stimulate the economy. Using 514 survey responses collected both in-person and online, the objectives of this study were to calculate the marginal propensity to consume (MPC) for Peruvian subsidy recipients and to evaluate the heterogeneity amongst beneficiaries based on four individual factors: pre-pandemic savings, financial inclusion (bank account ownership), survey response type (online vs in-person), and domicile location (residing in Lima Metro or not). Overall, survey responses showed an average MPC of 0.89, which was greater than subsidy-inspired MPC studies from high-income countries like the United States, United Kingdom, and Japan. There was a statistically significant relationship between MPC and liquidity, which corroborated previous studies on MPC from other countries. Relationships between similar programs in Peru and high-income countries for the impact, effectiveness, and purpose of direct stimulus payments are discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".