Empirical Analysis on the Influence of Business Environment on Foreign Direct Investment Inflow Based on the Panel Data on 26 Countries
Bibliographic record
Abstract
With the intensification of global competition and development of investment theories, foreign direct investment (FDI) is no longer solely affected by economic factors. Many noneconomic factors, such as policies and institution, now play an important role in FDI inflow. As a composite indicator, business environment has attracted a growing attention from investors. From theoretical and empirical perspectives, this paper quantifies and qualifies the influence of business environment over the FDI under different conditions. The impact mechanism of business environment on the FDI was refined by decomposing business environment into multiple subfactors, and considering various factors of different economies, such as natural resources (NR), technological resources (TR), and political stability (PS). An empirical analysis was conducted on the panel data of 26 countries in 2005-2018. The results show that: the host country can attract more FDI inflow by improving business environment, NR, TR, and PS; excessively high NR and TR, to a certain extent, suppress the promotion effect of business environment on FDI; four subfactors of business environment, namely, the protection of small and medium investor (PI), cross-border trade (CT), electricity supply (ES), and insolvency (IN), have relatively high promotion effects on FDI inflow. The research results enrich the theories on FDI and business environment, and provide a reference for the design of innovative polices.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".