Does the US Biofuel Mandate Increase Poverty in India
Bibliographic record
Abstract
In recent years, many countries have adopted aggressive policies that promote biofuels as a substitute for gasoline in transportation. For instance, 40 % of US grain is now used in transportation. This share is expected to rise significantly under the current Renewable Fuels Mandate. In this paper, we focus on the effect of the US mandate on poverty in India. First, we use a model with endogenous land use to estimate the effect of the mandate on the world price of selected food commodities, namely rice, wheat, sugar and meat and dairy, which provide almost 70 % of food calories in India, and fuel for transportation. We obtain world price increases of the order of 10 % for most of these commodities. Next we estimate their price pass-through to the Indian domestic market. Finally, using household data on Indian food consumption, wages and income, we estimate the effect on welfare. We account for the positive effects of food price increases through wages and income. We show that the net impact on welfare is negative and regressive, i.e., the policy affects the poorest the most. The current mandate may create about 35 million new poor in India alone. With imperfect pass-through of world prices food markets, this number declines to 14 million. The main implication is that even if biofuel policies lead to only a modest increase in food prices, they may cause a significant increase in poverty in developing countries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".