Foreign Direct Investment and Knowledge Diffusion in Poor Locations
Bibliographic record
Abstract
We use a plant level survey to identify interactions between domestic plants and foreign direct investment (FDI) in Ethiopia's manufacturing sector.One third of Ethiopian plants are linked to FDI through labor sharing, supply chains and competition.Technology upgrading most commonly occurs as a result of competition in output markets and observation and imitation of FDI in the same line of business.Other benefits include enhanced managerial practices and knowledge about exporting.Spillovers from FDI are identified by comparing changes in total factor productivity (TFP) among domestic plants in districts where a large greenfield foreign plant produces and districts where FDI in the same industry and around the same time was licensed but not yet operational.Over the four years starting with the year of the FDI opening, the TFP of domestic plants is 11 percent higher in treated districts, employment in domestic plants increases and more domestic plants open.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".