The impact of crime and other economic forces on Mexico's foreign direct investment inflows
Bibliographic record
Abstract
This paper examines the effect of different crimes on Foreign Direct Investment (FDI) inflows into the 32 Mexican states. Using a state-quarter panel data for the period 2005 to 2015, we estimate alternative models of FDI, with fixed effects throughout a flexible lag-lengths methodology and System Generalized Method of Moments (SGMM) models in order to identify the determinants of FDI inflows into the country. The dependent variable in our model is the annual inflow of FDI and the independent variables are state level indicators (real wages and electricity consumption), and macroeconomic forces (the real exchange rate and interest rate). We find that homicides and thefts have negative statistically significant effects on FDI, while other crimes show no effects. Partitions of the sample suggest higher negative effects in the most violent states.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".