Bibliographic record
Abstract
This paper estimates the impact of reducing export and import tariffs on firm input choices. In presence of borrowing constraints, lower export tariffs facilitate the reallocation of capital and labor inputs across firms, while a decline in import tariffs either tightens import competition or increases the availability of imported inputs; all three mechanisms suggest that a higher degree of openness should be associated with lower misallocation. To analyze the empirical relationship between openness and input misallocation, we draw on the annual surveys conducted by the Chinese National Bureau of Statistics (NBS) between 1998 and 2007. From the surveys, we con- struct firm-level measures of input misallocation that control for firm heterogeneity; we identify shocks to openness using industry tariff levels and firm trade shares. We find that firm facing higher tariffs in either import or export markets make less optimal input choices. We further decompose our analysis between input and output tariffs: our results suggest that the labor reallocation mainly occurs because of lower input tariffs, while the selection effect induced by changes in output tariffs does not necessarily cause more distorted firms to exit and, therefore, tends to have an insignificant effect on input allocation. Finally, we calculate the contribution of tariff changes towards aggregate misallocation and productivity: our results indicate that the impact of firm-level tariff reductions on aggregate misallocation and productivity was marginal in our sample period, but the presence of sizeable interactions between trade shocks and mis- allocation at the sector level suggests that our result should be interpreted as a lower bound of the overall effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".