Challenges of working with the Chinese NBS firm-level data
Bibliographic record
Abstract
Over the reform period, industry has been the source of 40% of GDP, and has contributed 90% of China's exports. Annual firm-level surveys that begin in 1992, complemented with industry-wide census in 1995, 2004 and 2008, are rich sources of data on firm behavior. It is well-known that working with Chinese data requires overcoming difficult measurement issues. Macroeconomic series, for example, are often suspected of suffering from reporting bias and political interference. Working with the firm-level data has its own challenges. In this paper, we provide an introduction to these data sets. We discuss and illustrate several of the issues that make comparability over time difficult and suggest solutions. The importance of a particular measurement issue often depends on the exact application. We illustrate this point by tracing the evolution of the relative productivity level of entrants and incumbents over time, distinguishing between changes in actual performance and changes driven by measurement problems. We conclude by identifying a few promising areas of future research and margins on which collaboration among users to improve these data might be beneficial.
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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 | 0.143 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".