Inequality and Trade Policy: Pro-Poor Bias of Contemporary Trade Restrictions
Bibliographic record
Abstract
This paper studies the pro-poor bias of contemporary trade policy in India by estimating the household welfare effects of eliminating the current protection structure. The elimination of a pro-poor trade policy is expected to have lower welfare gains or higher welfare loss at the low end of the per capita expenditure distribution. The paper first constructs trade restrictiveness indices for household consumption items and industry affiliations using both tariffs and the ad-valorem equivalent of non-tariff barriers. The welfare effects are estimated through its impacts on household expenditure and earnings. The results indicate that Indian trade policy is regressive through the expenditure channel as it disproportionately raises the cost of consumption for poorer households, while it is progressive through the earnings channel in urban areas and neutral in rural areas. The net distributional effect through these two channels is estimated to be regressive, and elimination of current trade protection structure is expected to reduce inequality. These results indicate that a trade protection structure that designed as a progressive trade policy through the earnings channel may induce price effects that are regressive through the expenditure channel.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".