Examining the effect of removing banknotes and implementing Goods and Services Tax on access to healthcare in India
Bibliographic record
Abstract
This paper explores the effect of the banknote ban and GST, which were implemented within a span of 8 months in 2016-2017, on self-reported illnesses and healthcare consumption in India. The findings show that the increasing trend of self-reported illness from 1995-96 to 2014, reversed in 2017-2018. The reduction in healthcare consumption between 2014 and 2017-2018 is primarily within the private care sector rather than public care, indicating economic duress induced by the banknote ban and GST as its cause. The reduction in self-reported illness and healthcare consumption is more prolonged among the occupationally vulnerable. The reduction in reporting of illnesses, healthcare consumption, and out-of-pocket expenditure on health continued even after one-and-half years of the banknote ban; this may also have been exacerbated later by the implementation of GST. Data indicate that the condition of public healthcare deteriorated between 2014 to 2018. As a result, even though the weaker sections’ access to private healthcare diminished, they could not turn to public healthcare either, resulting in a reduction in overall healthcare consumption. The findings of this paper call for a robust, functioning, affordable public healthcare system in India for greater crisis resilience.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".