Capital-Labor-Energy Substitution in Nested CES Production Functions for China
Bibliographic record
Abstract
In the CGE based policy modeling literature, especially recent literature on policy modeling for global climate change, nested CES production functions over multiple inputs have been widely used. Although lack of reliable estimates of substitution elasticities for nested structures has been acknowledged for a long time, the problem has not yet been solved satisfactorily. This is especially the situation for the Chinese case for which modeling work has global implications. This paper reports estimates of substitution elasticities for normalized nested CES aggregate production functions for China with different nested structures of input factors: capital, labor with or without human capital adjustment, and energy using data for the period 1979-2006. We adopt grid search based non-linear optimization techniques for estimation. The results show that all the substitution elasticities we estimate are positive. For the widely used (K,L)E structure, we find that the substitution elasticity between capital and labor for China is below unity. When human capital adjusted labor is used as input instead of unadjusted raw labor, estimates of substitution elasticity between capital and labor become lower. By considering the significance of estimates, our results suggest that the (E,L)K structure seems more appropriate for the Chinese economy.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".