The Location Choice of Foreign Direct Investment and Economic Development in Africa
Bibliographic record
Abstract
Foreign Direct Investment (FDI) inflow to Africa has unevenly distributed investment location choices of multinational enterprises because of some exogenous economic factors associated with the locations, which vary across countries in Africa. The data used in the paper comes from Financial Times, World Bank, African Development Bank. This paper investigated what determines the location choice of FDI inflow to Africa using data on 3,768 firms from 88 countries making location choices in 54 African countries using a multicategory logistic regression. The findings show that: (1) the natural resource seeking enterprises invest more in landlocked countries relative to manufacturing and tertiary sector; (2) the natural resource seeking firms are less concerned about local market size and location’s economic condition comparing to manufacturing and service industries; (3) despite the accusation against the multinational enterprises (MNEs) for exploiting Africa’s natural resources, most of the MNEs choose locations with a large market size and better economic development; (4) the MNEs from developed economies prefer the location with a large market size and a better-developed economy comparing to those from the developing economies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".