Does Institution Affect the Inflow of FDI? A Panel Data Analysis of Developed and Developing Countries
Bibliographic record
Abstract
The objective of this paper is to study the institutional impact on the net FDI inflow along with the other possible determinants of Foreign Direct Investment (FDI) in 40 countries comprising of developing and developed countries over the period of 1990-2010 by using panel econometric model. The dependent variable of our study is log of net FDI inflows measured at current US million dollars of different countries in different points in time and independent variables are log of GDP measured at current US dollars, total trade as a share of GDP, gross fixed capital formation as a share of GDP, inflation as measured by consumer price index (annual %) and log of composite index for infrastructure and a number of institutional variables such as investment profile, law and order and bureaucratic quality. According to the econometric results, the coefficients of log of GDP, trade to GDP ratio, gross fixed capital formation (% GDP), and log of composite index for infrastructure and institutional variables are positive and significant but coefficient of inflation (%, CPI) is negative and significant. Moreover, the institutional variables- investment profile and law and order have positive effect on FDI and bureaucratic quality has negative effect and also statistically significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".