Elite Capture : Residential Tariff Subsidies in India
Bibliographic record
Abstract
India - home to one of the world's largest populations without electricity access - has set the ambitious goal of achieving universal electrification by 2017. 311 million people, a quarter of its population, remains without power, despite substantial efforts to increased affordable access for the poor. This study focuses on India's residential electricity subsidies, as viewed through a poverty lens. Addressing these issues is especially urgent since the residential electricity sector accounts for nearly a quarter of India's total electricity consumption. Comparison of two survey rounds (2004/05 and 2009/10) was used to assess changes in electricity consumption over time. The study approach analyzed subsidy distribution by both below poverty line (BPL) and above poverty line (APL) grouping, as well as income quintile, to allow for the wide variation in poverty rates states. The key findings in this study are that 87 percent of subsidy payments go to APL households instead of to the poor, and over half of subsidy payments are directed to the richest two-fifths of households. Furthermore, these estimates are conservative because they assume that BPL and APL households are accurately identified. Because APL households tend to consume more electricity, subsidies are skewed toward the upper quintiles. The major driver of these outcomes is tariff design. Few states have highly concessional BPL tariffs; in most, all households are eligible for a subsidy on at least a portion of their monthly electricity consumption. Combined with the fact that the poorest households consume relatively small amounts of electricity means that wealthier consumers with electricity access are typically eligible for just as much, if not more, subsidy as poorer ones. India's states have a variety of available options for improving their subsidy performance. Certain states model good practices that other states could consider adopting, for example, Punjab, Sikkim, Chattisgarh, and others. States may consider four model tariff structures that meet the twin, medium-term policy goals of high subsidy targeting and low cost. These are (i) creating BPL tariff schedules and eliminating subsidies from other schedules, (ii) delivering subsidies through cash transfers instead of tariffs, (iii) creating a volume differentiated tariff (VDT), and (iv) creating a lifeline tariff and removing subsidies from other tariffs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".