The relation between external governance environment and over-investment: Evidence from industry regulation
Bibliographic record
Abstract
Based on the Law and Finance theory, and the regulatory capture theory, external governance environment and industrial regulations can exert a certain influence on corporate overinvestment.On the basis of qualitative analysis of the relationship between external governance environment and corporate over-investment under different industrial regulation conditions, this paper, using data of non-financial companies listed in Shanghai and Shenzhen Stock Exchanges in the period 2001-2010, describes the regional distribution characteristics of over-investment of Chinese listed companies, and establishes an OLS regression model of the relationship between external governance environment and over-investment.The study respectively groups data from regulated and non-regulated industries as a sample and empirically tests the OLS regression model.Results show that: from the perspective of economic geography, there exists a local spatial cluster phenomenon in the distribution of over-investment of listed companies in regulated industries, while non-regulated industries conform to no regularity.In regulated industries, external governance environment factors (level of government intervention, rule of law and financial development) may exert a significant negative influence on the degree of over-investment of listed companies, but on non-regulated industries, their effect is reversed.Also, government intervention, legal enforcement and financial development are positively correlated to over-investment.Further research indicates that, compared with government intervention and financial development, legal enforcement influences over-investment the most.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".