Rule by Law, Government Control and Company Investment Efficiency: Empirical Evidence from China
Bibliographic record
Abstract
Under the premise of considering the motives and actions of all levels of governments, this paper empirically studies the mechanism and economic consequences of the law and order regulating inefficient investments on a sample of 3201 firm-year observations of listed companies in Shanghai and Shenzhen Stock Exchange in China over the period from 2007 to 2009. Using investment-cash flow sensitivities to proxy for inefficient investment of a company, I provide evidence that the degree of inefficient investments of listed companies controlled by local governments is much higher than that of other companies controlled by central government or non-governments. Furthermore, I find that the level of law and order of a region with high quality can reduce significantly the sensitivity of investment of Chinese listed companies to cash flows, the effect of which is much stronger for listed companies controlled by local governments. According to the conventional interpretations, a lower investment cash flow sensitivity means less investment distortions. However, the improvement of investment efficiency aising from the law and order are not ultimately transferred to the increase in the company’s future operating performances, suggesting that the roles of the level of law and order of a region across China playing in controlling company’s inefficient investment are limited.
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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 | 0.000 | 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".