Wage Spillovers from Foreign Direct Investment in Kenya’s Manufacturing Sector
Bibliographic record
Abstract
The objective of this paper is to investigate the effect of foreign direct investment on average wages paid by domestic manufacturing firms in Kenya. Specifically, the paper aims at identifying the transmission channels through which wage spillovers from foreign direct investment occur as well as the impact of technology gap and firm size on the behaviour of the spillover transmission channels. Employing panel data obtained from the World Bank Enterprise Surveys covering the period 2007–2018 and using fixed effects and Two-Step System GMM, we analyzed both horizontal and vertical spillover channels for wage spillovers. Findings from estimations based on all domestic firms indicated that there were no significant wage spillovers from FDI. However, when the technology gap was considered, domestic firms with low technology gaps with foreign-owned firms showed statistically significant positive wage spillovers via backward linkage, demonstration effects, and labour mobility channels and statistically significant negative spillovers via the competition effects channel. Finally, the results showed that firm size had no impact on the behaviour of various wage spillover transmission channels examined.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".