The determinants of gender spillovers in FDI: An analysis of Namibia
Bibliographic record
Abstract
This paper examines the effect of foreign firm characteristics on FDI spillovers in Namibia, with a specific focus on gender spillover, as an additional measure to total labour spillover. Also, the possible differing spillovers in the manufacturing and services firms are investigated. Using data from the World Bank Enterprise Survey for 2006 and 2014, the paper finds that the gender spillover is significantly influenced by four variables; sector, firm age, transport challenges, and supply-chain finance. Relative to the services sector, firms in the manufacturing sector are found to significantly drive gender spillovers due to a probable preference for females “nimble fingers”, perceived obedience, and lower wages. However, the paper documents that older firms, especially those in the manufacturing sector, negatively impact gender spillovers. Also, transport challenges are shown to drive gender spillovers. This result arises from the possibility that they impose an opportunity cost, whereby, firms may defer investing in female labour due to lower perceived wages to compensate for high transport costs. Finally, we document a negative influence of supply-chain finance on gender spillovers, arising from possible labour cuts as MNCs outsource jobs in the supply chain.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".