Demand-Side Determinants of Billing Efficiency in India: A Panel GMM Approach
Bibliographic record
Abstract
The power sector’s efficiency is paramount in a country such as India, where electricity consumption and access have been tantamount to economic growth. The study investigates whether Billing Efficiency is affected by Per Capita GSDP. A panel has been constructed using data from 17 major states from 2011-12 to 2021-22. The results, obtained using the Generalized Method of Moments (GMM) regression, suggest that Billing Efficiency increases when there is an increase in Per Capita GSDP. People’s affinity to evade paying the bill decreased when their incomes rose. Per Capita Consumption of Power, which the study considered the control variable, exhibited no impact on Billing Efficiency. Estimates indicate an increase of Rs. 10,000 per annum in Per Capita GSDP will increase Billing Efficiency by 0.31%. High-income states showed higher billing efficiency; the intuitive opposite was true for low-income states. As best practice, long-term investment in infrastructure can be a robust solution to reduce the leakages in Input Energy gradually.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".