Economic Stimulus and Financial Instability: Recent Case of the U.S. Household
Bibliographic record
Abstract
The effectiveness of government policies and economic stimuli during the 2007 financial crisis and the COVID-19 pandemic are compared in this study. While the 2007 financial crisis started in the real estate market and spread through the contagion effect to other sectors, the pandemic halted the all sectors of the global economy simultaneously. In the United States, where the social safety net is not as strong as other advanced economies, the unemployment rate skyrocketed and many families lost income. The federal government countered with various relief packages, which have been, unlike the rounds of quantitative easing prevalent after the 2007 financial crisis, direct payments to households and businesses. The Agent Instability Indicator and default elasticity coefficient are used to quantitatively assess the financial instability and default risk of subgroups of United States households classified by percentile of income and net worth. It turns out that the financial instability level of the United States household during the pandemic has not been as high as that during the 2007 crisis and the Great Recession. It is concluded that the direct handout of cash—so called helicopter money—is more effective at preventing financial collapse and stabilizing the economy than quantitative easing through asset purchase.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".