What Drives the Volatility of Firm Level Productivity in China?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The enterprise reforms of the 1990s profoundly changed the structure of the economy in China. Using a fi rm-level dataset collected annually during the period of 1998-2007, this paper examines the variation of productivity volatility across fi rms of different characteristics as well as its evolution over time, and investigates the sources of productivity volatility at the fi rm level. The results suggest that in general, productivity volatility at the fi rm level declined over time in China. Large fi rms, old fi rms, foreign fi rms, and fi rms located in the coastal provinces are less volatile. Firm size and location are the two major factors that drive changes in productivity volatility -one in a positive way and one in a negative way. While the gaps of volatility between smaller fi rms and larger fi rms declined, the gaps between fi rms located in the coastal provinces and inland provinces increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it