Bibliographic record
Abstract
Purpose This study uses the quarterly data of the Korean economy from 1990 to 2019 to identify the dynamic impacts of the expansions of export on employment. Design/Methodology/Approach The empirical analysis of this paper uses a methodology that applies the rolling regression model based on the dynamic OLS. The 40 quarters are composed of one sample, and then a total of 80 samples are formed by moving in 1 quarter afterward, starting from the first quarter of 1990. Findings It is found that export expansion since 1990 has little or even negative impact on employment, and the time-varying impacts are nor stable over time. In addition, the impact of export expansion on job creation is asymmetry. That is, the statistical significances are not observed in the samples where export expansion have positive impacts on employment, whereas the samples where negative impacts are generally statistically significant. In recent years, the impact of export by processing stage on employment is differently estimated Research Implications The empirical results suggest that the direct impact of export on employment in the Korean economy since the late 1990s is limited. In recent years, the impact of job creation have been observed in exports of consumer goods and capital goods compared to those of intermediate goods. This suggests that diversification of export products can be effective for job creation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".