Impact of political conflict on foreign direct investments in the mining sector: Evidence from the event study and spatial estimation
Bibliographic record
Abstract
This study aims to investigate the impact of conflict on greenfield foreign direct investment (FDI) in the mining sector covering the period of the 1st quarter of 2003 until the 3rd quarter of 2017, across 151 countries. Unlike previous works, this paper focuses on testing two impacts. First, we test for a dynamic impact to uncover the effect of conflict on FDI over the contemporary and subsequent annual quarters. Second, we test for a spatial spillover impact. To achieve these goals, we apply both a panel spatial approach and an event study analysis, using a unique proprietary database FDIMarkets. The main findings are as follows. First, the presence of a dynamic impact depends on the intensity of the conflict for the particular country group, with higher levels of intensity being associated with a higher probability of the presence of a dynamic effect. Second, we find a significant negative spillover impact of greenfield mining FDI of neighbouring countries on the greenfield mining FDI of the FDI-receiving economy. We do not find, however, that conflict in neighbouring countries has a spatial spillover impact on greenfield mining FDI of the FDI-receiving economy.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".