Investigating Demonetisation’s Impact and India’s Prospects as a Cashless Economy
Bibliographic record
Abstract
This paper aims to analyse the effect of demonetisation on the different sectors of economy and throw light on the various challenges before the government of India to make create cashless economy. Government of India’s banned the old 500 and 1000 notes with an aim to resolve against corruption, black money, terrorism and counterfeit notes. But in last couple of months, the country has witnessed some recent terrorists’ attacks (around 7) like Nagrota Attack and Bhopal passenger train blast, a large number of fake currency printing cases, cases of converting black money into with the help of some bank employees or by buying foreign currency, Gold, property etc. This has jolted the people’s belief in government’s this decision. India’s GDP growth slipped to 6.1% in the fourth quarter of year 2016-17, compared with 7.1% in the previous quarter. Also, GVA growth at constant prices declined to 5.6% in the fourth quarter of year 2016-17, clearly showing the scars of demonetisation on the economy. Our analysis shows that out of twenty two industry groups in the manufacturing sector, 5 industry groups registered positive growth in December, 2016. Also, India’s annual infrastructure output growth slowed to 3.4 per cent in January from 5.6 per cent in December. Sectors like agriculture, real estate, FMCG, automobile and infrastructure have been badly affected. Moreover, the value of non-cash transactions in May 2017 is closer to the value in November 2016 at Rs 94 trillion, when demonetisation started. Still, there are a number of challenges before the government to make India a cashless economy. This requires the government of India to really work on ground realities to make this dream a reality.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".