The Political Economy of Commodity Export Policy: A Case Study of India
Bibliographic record
Abstract
Many developing country governments discriminate against sectors that export primary commodities. India, for example, discriminates against cotton production. Exports of cotton have been restricted by quotas, and the mill industry has been subject to such regulations as the obligation to supply hank yarn for Indian handlooms. These interventions have led to stagnating cotton yields, rent-seeking activities, manipulation of cotton statistics, and low profitability in cotton mills'offsetting the short-run benefits of inexpensive cotton in India. The author develops a numerical model to measure the impact of liberalizing cotton exports. This is the first simulation model of its type, and the first multimarket model that computes price elasticities endogenously, based on the ratios between product prices and input costs. The model distinguishes short-run from long-run effects by drawing on the principle that the cost of capital varies only in the long run. Results of the simulation under complete liberalization indicate heavy (16 percent) net losses in income in the handloom sector. The government subsidies needed to compensate for those losses amount to US$423 million, or about 25 percent more than current government revenue in India's cotton sector. Such costly subsidy of handlooms is undesirable not only budgetarily but also politically, becauseit creates new vested interests. The author proposes politically feasible programs for managing the adverse impact of liberalization on the handloom sector, including handloom conversion and involvement of mills in cotton cultivation. Governments tend to prefer an export quota to an export tax because it is easier to change a quota than a tax rate if market conditions change. But flexible controls actually facilitate rent-seeking activities. As quotas are changed more often than tax rates, more interest groups get involved in lobbying and in padding crop estimates. In other words, the political and economic problems that result from restrictions on commodity exports can be more serious than those relating to resource misallocation. It is important to consider how policy changes will affect the political power structure and the objectives of different interest groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".