Evaluating the Economic Impacts of the G20 Compact Initiative: Evidence from Causal Inference Using Advanced Machine Learning Techniques
Bibliographic record
Abstract
The G20 Compact with Africa (CwA) initiative, launched in 2017 under the German G20 Presidency, aims to enhance the attractiveness of private investment in Africa by improving member countries’ macro, business, and financing frameworks. This study evaluates the CwA initiative's impact on FDI, GDP per capita, gross capital formation, exports, and employment using targeted maximum likelihood estimation. In the initial Q model, we employed machine learning models like Random Forest, Gradient Boosting, and XGBoost to estimate the outcome given the covariates. Subsequently, we used OLS to update the initial estimate through the clever covariate to improve the efficiency and accuracy of the estimated treatment effect. Our findings indicate that the CwA initiative is significantly associated with increased FDI and export growth in member countries, but these gains have not yet led to broader economic growth, such as improvements in gross capital formation and GDP per capita.
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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 | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".