The impact of informatization on total factor productivity and its regional differences: An empirical study based on Chinese data
Bibliographic record
Abstract
On the basis of a theoretical analysis, this paper develops an indicator framework for measuring informatization level and estimates the informatization levels of various regions in China spanning 2006 to 2019; Utilizing the DEA-Malmquist index approach, the paper estimates the TFP of various regions in China and decomposes it into technological progress and technical efficiency. The findings reveal an annually increasing level of informatization in China, with higher presence in the east and lower in the west. The influence of informatization on TFP presents a positive "U" -shaped feature, but the turning point of the eastern region is earlier than that in the central and western regions. The level of informatization also has a spatial spillover effect on TFP, similarly presenting a positive "U" shape. The article offers pertinent policy suggestions aiming to promote China's TFP through informatization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".