Stimulating economy via fiscal package: The only way out to save vulnerable Workers' lives and livelihood in Covid‐19 pandemic
Bibliographic record
Abstract
This paper examines critically the economic package announced by the Indian central government to counter the challenges of lives and livelihood in the Covid-19 pandemic. This paper estimates the shares of the fiscal economic packages in two phases as per the shares of the vulnerable workers and number of Covid-19 cases in the Indian states. The recent data on labour market are used from National Sample Survey Organization and data on Covid-19 cases from Ministry of Health and Family Welfare. This paper recommends alternatively a fiscal stimulus package of Rs. 10 lakh crores (5% of GDP) with an immediate effect to counter the present problems of health, food and unemployment in the pandemic and should be extended to Rs. 24 lakh crores (12% of Indian GDP) to the Indian states for at least 1 year to protect the lives and livelihood of the most vulnerable, informal and migrant workers. The populous and poor states like Uttar Pradesh and Bihar have higher share of vulnerable workers and highly industrialized states like Maharashtra, Gujarat, Delhi and Tamil Nadu have higher number of Covid-19 cases. Due to the unplanned lockdown in India, there has been a surge in Covid-19 cases across the country that in turn led to an increase in vulnerable workers in poor states due to reverse migration from industrialized states to populous and poor states during the pandemic. Furthermore, the paper explains the five significant factors that justify the adoption of an expansionary fiscal policy rather than monetary policy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".