The effects of fuel subsidies on regional income distribution through smuggling
Bibliographic record
Abstract
Abstract Fuel subsidies, intended to improve consumer affordability, can result in economic distortions through altered relative prices and negative environmental impacts. Furthermore, these subsidies can lead to unintended consequences, such as fuel smuggling, especially to neighboring countries where significant price differences exist. While previous research has extensively explored the economic dimensions of fuel subsidies, the potential interplay between fuel smuggling and its impact on regional income distribution remains understudied. This study investigates the effects of fuel smuggling, stemming from Iran's long history of significant fuel subsidies, on income distribution across all 30 provinces of the country. We employ a model to specify the demand for fuel smuggling, using fuel prices in neighboring countries and the distance to the nearest border as sources of identification. Subsequently, we estimate the monthly smuggling profit across regions and assess this profit's influence on regional income distribution. Our empirical analysis draws on monthly data on gasoline and diesel sales from 160 fuel distribution districts in Iran, spanning the period from 2005 to 2014. Our findings demonstrate specific cases of smuggling activities that account for an average of 25% of total fuel consumption and generate substantial income in economically disadvantaged border provinces. We discuss the socioeconomic implications of fuel subsidies, with a focus on smuggling activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".