Does Relation between Energy and Economic Growth hold for all Indian States? Empirical Analysis using Hurlin-Venet Process
Bibliographic record
Abstract
Electricity consumption is often regarded as a precondition for economic growth and any bottleneck in its production can severely hurt the growth prospects of an economy more specifically a developing one. Representing a strong case of its value, the causal relationship between energy consumption and economic growth is addressed by extending the Granger causality framework in a heterogeneous panel setup. Exclusively four different causal behaviours are examined: Homogeneous Non-Causality (HNC), Homogeneous Causality (HC), Heterogeneous Non-Causality (HENC), and Heterogeneous Causality (HEC). Both HNC and HC hypotheses are rejected in the causality direction from Economic growth to energy consumption thereby suggesting that the panel of Indian states is not homogeneous. Following this heterogeneous causality tests (HENC and HEC) are conducted for each Indian state to check the hypothesis of causality from economic growth to energy. For 8 out of 17 Indian states strong unidirectional causality is found while for 6 other states, there is no evidence of any causality in the stated direction. The remaining 3 states show weak evidence of causality. Thus, the results are suggestive of the fact that the central government cannot dictate policies at the state level rather state needs to frame regional policies in line with the situation that suits.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".