Statistical Capacity, Human Rights and FDI in Sub-Saharan Africa Patterns of FDI Attraction in Sub-Saharan Africa
Bibliographic record
Abstract
Foreign Direct Investment (FDI) is commonly perceived as one of the main drivers of technological progress and socio-economic development. At the same time, FDI is often regarded as an instrument of stabilising authoritarian regimes, which disenfranchise the rights of citizens to increase rents generated by foreign firms. Given that both views are accurate, the improvement of human rights and economic development could constitute two conflicting goals. This particularly applies to Sub-Saharan Africa, where a sizeable number of countries are mired in poverty and governed by authoritarian power structures. In evaluating the importance of these soft factors, we examine two important institutional factors of FDI attraction: We address the question of whether human rights violations deter FDI attraction and explore whether FDI depends on the amount of available socio-economic information about the country to be invested in. For the latter, we use a novel variable, namely the Statistical Capacity Figures of the World Bank, which depicts an indicator of effectiveness of the national statistical systems. In order to analyse the relationship between human rights and FDI, we run a regression model covering 41 Sub-Saharan countries covering the years from 2006 to 2015.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".