Regulations, Business Taxes, and Foreign Direct Investment
Bibliographic record
Abstract
Abstract: In this study, we apply three sets of econometric models to examine the effects of business regulations on foreign direct investment (FDI) by using FDI statistics from 12 source countries to 64 host countries in 2000. Our log-linear results suggest that FDI inflows are strongly correlated with business regulatory costs in the FDI host countries. By using the endogenous threshold models of Hansen (1996, 1999, 2000) and the rolling-regression techniques of Rousseau and Wachtel (2002), we find evidence of a nonlinear threshold effect in the relationship between FDI inflows and regulatory costs. When a host country’s regulatory costs are sufficiently low, a further decrease in regulations may not stimulate and, in fact, may even decrease FDI inflows. On the other hand, beyond some threshold, FDI inflows significantly rise as the regulatory costs fall. In addition, we find that the marginal effect of business taxes on FDI depends on the level of regulatory costs; i.e., as regulatory costs rise, the marginal effect of taxes on FDI inflows falls. Our results suggest that the regulatory competition between FDI host countries may have different impacts on countries with different regulatory cost levels. While a fall in the costs can directly stimulate FDI inflows in heavily regulated countries such as Brazil and China, it might have no effect, or even a negative effect, on FDI inflows in low-cost countries such as Canada and the United States. In the low-regulatory-cost countries, tax incentives might be more effective to attract FDI than those in heavily regulated 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".