Alternative corporate governance: Does tax enforcement improve the performance of mergers and acquisitions in China?
Bibliographic record
Abstract
Abstract Research Question/Issue We study whether tax enforcement can function as a corporate governance mechanism in emerging countries with weak tax enforcement. In the case of China, we examine whether and how external monitoring by tax authorities constrains insiders' opportunistic behavior in corporate mergers and acquisitions (M&As). Research Findings/Insights We employ the implementation of the third stage of the China Tax Administration Information System (CTAIS‐3) as a quasi‐natural experiment and adopt a difference‐in‐differences (DID) approach. We find that strengthening tax enforcement by CTAIS‐3 can improve the efficiency of M&As by reducing agency problems in the decision‐making process. Our conclusions remain unchanged under a series of robustness checks. Moreover, the results show that the impact is mainly observed in regions with stronger local government taxation motivation and in firms with poorer internal or external governance and poorer accounting information. Theoretical/Academic Implications We find that strengthening tax enforcement can improve M&A decisions even in emerging markets, which provides direct evidence for the predictions from theory that tax authorities play a governance role in supervising corporate insiders. Our paper also extends the literature on the determinants of M&A performance from the perspective of tax authorities. Practitioner/Policy Implications This study has policy implications for governments around the world to improve corporate governance by strengthening tax enforcement. The Chinese government applying advanced information technology to tax enforcement can provide a reference for other countries.
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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 | 0.002 | 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 | 0.001 | 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) | 0.002 | 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".