Economic Freedom, Fiscal Rules on FDI Inflows: An Analysis of 24 Developing Countries
Bibliographic record
Abstract
Foreign Direct Investment (FDI) plays a crucial role in enhancing economic growth and development. It brings capital, technology, managerial skills, and employment opportunities to host countries. However, attracting FDI requires a conducive business environment characterized by economic freedom and effective fiscal institutions. This paper aims to explore the relationship between economic freedom, a new type of fiscal institutions named fiscal rules, and FDI inflows. It provides a comparative analysis of different countries and investigates the mechanisms through which economic freedom and fiscal institutions influence FDI. The panel data analysis employed in this study utilizes two estimation methods, namely the Random Effects Model (determined through the Hausman test) and the Two Stage Least Squares Method (to address endogeneity concerns). The empirical findings reveal several noteworthy insights. Firstly, GDP, trade openness, and gross fixed capital formation exhibit a positive relationship with FDI inflows, while inflation does not have a significant impact. Additionally, our research indicates that specific economic freedom sub-indicators, such as tax burden, monetary freedom, trade freedom, and financial freedom, positively influence FDI inflows. Conversely, the presence of expenditure rules is found to have a negative impact on FDI inflows. Furthermore, we explore the interactive effects of fiscal rules and economic sub-indicators on FDI inflows, providing further insights into the relationship between these factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".