Causal Nexus between Billing Efficiency and Economic Growth in India: An Analysis Using Vector Autoregression
Bibliographic record
Abstract
Energy being a major factor of production plays a pivotal role in inducing a sustained and high economic growth of an economy. This paper attempts to examine the Vector Autoregression (VAR) based Granger causality between billing efficiency and growth rate of per capita Gross Domestic Product (GDP) of India using annual time series data for the period 1970-71 to 2014-15. The conventional Augmented Dicky-Fuller and Phillips-Perron tests reveal that both the series are non-stationary and individually integrated of order one. Johansen-Juselius cointegration approach finds no evidence on the long-run equilibrium association between these variables. However, the VAR - based Granger causality approach reveals unidirectional causality running from billing efficiency to economic growth without any feedback effect. As regards policy implication, implementation of both the short term and long term measures in improving billing efficiency, through the enhancement of commercial and operational efficiency in electricity distribution sector will undoubtedly aid in achieving sustained and high economic growth in India.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".