Firm productivity and importing: Evidence from Chinese manufacturing firms
Bibliographic record
Abstract
Abstract This paper investigates various aspects of the relationship between firm productivity and importing for a large sample of Chinese firms between 2002 and 2006 making a distinction between the origin, variety, skill and technology content of imports. Employing a random effects probit model and a propensity score matching with difference‐in‐differences (PSM–DID) approach and treating imports as endogenous in our measure of total factor productivity (TFP) (De Loecker 2007), we test the self‐selection and learning‐by‐doing hypotheses. Our results show evidence of a bi‐directional causal relationship between importing and productivity. Although importing firms tend to be more productive before entering the import market, once they start importing firms experience significant productivity gains for up to two years following entry. We also find evidence of learning effects following the decision to import, which is stronger when import starters source their products from high‐income economies, import a wider variety of products and import products with a higher skill and technology content. A number of robustness checks confirm the learning effects of importing on TFP growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".