The Aggregate Importance of Intermediate Input Substitutability
Bibliographic record
Abstract
We estimate long-run elasticities of substitution between intermediate inputs for Indian manufacturing plants. India's trade liberalization in the early 1990s provides an ideal natural policy experiment, with permanent and heterogeneous tariff reductions inducing changes in relative prices which we use for identification. We find a high degree of substitutability at the plant-level between 8 broad categories of material inputs, significantly above the Cobb-Douglas benchmark of 1. In contrast, we find elasticities less than 1 between energy, materials, and services as well as between value added and intermediates. We embed our elasticities in a general equilibrium model with a rich input-output structure to quantify their importance. Relative to a Cobb-Douglas benchmark, the aggregate gains from trade are 9% larger when intermediate inputs are substitutes, and come hand in hand with 40% more reallocation of labor across sectors. Furthermore, the aggregate gains from closing the India-U.S. TFP gap in any one sector are on average 29% larger with our estimated elasticities; losses from misallocation of intermediate inputs are more than 3 times larger.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".