Democracy in the neighborhood and foreign direct investment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The determinants of foreign direct investment (FDI) have been extensively studied. Even though there is extensive research in the area, most of it is based on analyzing the effects of host country characteristics on FDI flows, and yet there is little research on how neighboring country characteristics play a role in facilitating FDI flows to host countries. This paper analyzes the association between the democracy level in neighboring countries and FDI flows to host countries. Using bilateral FDI flows from the OECD countries, with a large host country sample, we find that countries surrounded by democratic countries attract higher FDI flows. Furthermore, we find evidence that countries that are surrounded by neighboring countries with good institutions tend themselves to have better institutions, experience lower civil conflict, and have higher political stability and hence indirectly attract higher FDI flows. Our findings suggest that if neighboring countries act in such way as to become more democratic, FDI flows to these countries would be higher since not only does improving the quality of democracy attract more FDI inflows, but also being surrounded by neighboring advanced democratic countries will also lead to higher FDI flows to them.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it