Do Trade and Investment Agreements Promote Foreign Direct Investment within Latin America? Evidence from a Structural Gravity Model
Bibliographic record
Abstract
Latin America has experienced a surge in foreign direct investment (FDI) in the last two decades, in parallel with the ratification of major regional trade agreements (RTAs) and bilateral investment treaties (BITs). This paper uses the latest developments in the structural gravity model theory to study if the co-existence of BITs and two major regional agreements, Mercosur and the Latin American Integration Association (ALADI), exerts enhancing or overlapping effects on FDI for eleven countries in Latin America over the period 1995–2018. The study is novel as it accounts for variations in the degree of investment protection across BITs within Latin America by computing a quality index of BITs. It also explores the nature of interactions (enhancing/overlapping effects) between RTAs and BITs. The findings reveal that belonging to a well-established regional trade agreement, such as Mercosur, is significantly more effective than BITs in fostering intra-regional FDI. Phasing-in effects are large and significant and there is evidence of enhancing effects. Results within the bloc are heterogeneous: BITs exert a positive, but small effect, for middle income countries. However, BITs are not effective in attracting FDI in the case of middle to low income countries, unless these countries ratify BITs with a high degree of investment protection.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".