The Rise and Fall of Chinese Tax Incentives and Implications for International Tax Debates
Bibliographic record
Abstract
China had no foreign direct investment (FDI) before 1979. Now, it is one of the world's largest recipients of FDI. China has been generous to a fault in granting tax incentives to foreign investors. As of January 1, 2008, however, these FDI-specific incentives will be abolished or phased out. What explains the rise and fall? Were the tax incentives not effective in attracting FDI and promoting China's economic growth? What are the implications of the Chinese experience for international tax debates? This article examines these questions. Part II of the Article provides an overview of the Chinese tax incentive regimes for FDI. It briefly discusses the creation, expansion, and termination of tax incentives and the key motivations at each stage. Part III evaluates these incentives in terms of their effectiveness, efficiency and fairness. Effectiveness is examined on the basis of general data about FDI growth in China and empirical research on investors' reactions to Chinese tax incentives. The economic efficiency of tax incentives is assessed by looking at the positive externalities of FDI in China, the un-intended distortions to investment behaviour, and the extent to which the incentives lead to tax discrimination against local business. The equity aspect of tax incentives is assessed in terms of the role of tax policy in achieving redistributive justice in China. Part IV explores the implications of the Chinese experience for the debate on the use of tax policy in attracting FDI, harmful tax competition and international redistribution. Part V concludes the paper.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".