Ownership Characteristics, Real Exchange Rate Movements and Labor Market Adjustment in China
Bibliographic record
Abstract
This paper uses a firm level multi-industry data set covering 456 Chinese manufacturing sectors to assess the implications of Renminbi (RMB) real exchange rate appreciation for adjustments in employment and wage rates.We stress differences in both industry and firm characteristics within sectors.Our empirical results show that modest (and also larger) RMB real exchange rate appreciation would likely have pronounced effects on both net employment and wage rates.A 10% RMB appreciation would likely cause a net employment decline in Chinese manufacturing industries of between 4.1% and 5.3%, and a wage rate drop of 4% after controlling for other factors.Real exchange rate change effects by industry on net employment and wage rates vary significantly with the ownership characteristics of firms within industries.Employment and wage rates for private enterprises are less responsive to RMB real exchange rate fluctuations than is true for state owned enterprises (SOEs) and foreign invested enterprises (FIEs).This finding is opposite to the widely held belief that the labor market behavior of Chinese SOEs shows stronger labor market rigidities than for private firms.Impacts of exchange rate movements emerge as systematically related to export openness, overall import penetration and profit margins of individual manufacturing industries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".