Observing FDI spillover transmission channels: evidence from firms in Uganda
Bibliographic record
Abstract
We observe and analyse three intra-industry foreign direct investment (FDI) spillover transmission channels using unique firm-level data collected from on-site interviews and observations regarding domestic and foreign firms operating in Uganda in 2015. Our main results are: (1) the spillover effects mainly depend on the channel(s) by which they occur (the competition channel is most important while spillover benefits through the worker mobility and the imitation channels are less prevalent) and (2) both positive and negative spillover effects occur within the same channel and, moreover, effects differ by channel for the same case. These are novel and challenging findings that have not yet been recognised in theoretical and empirical research on FDI spillovers. Our results suggest that long-term pecuniary spillover effects are predominantly stimulated via the competition channel and show that only limited short-term and long-term technological spillover effects occur through the imitation and the movement of workers channels. These channels are not only less prevalent, but also appear to be constrained by competition-determined spillovers. We are confident that these directions for future research will have a high pay-off because, as shown by this exploratory fieldwork, a more complete picture of the spillover effects is reached when the channels are considered simultaneously.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".