Multinational Enterprises, Technology Diffusion, and Host Country Absorptive Capacity: A Note
Bibliographic record
Abstract
Abstract Previous empirical studies show mixed support for the hypothesis that the impact of technology diffusion from multinational enterprises (MNEs) on host country productivity growth depends on host country absorptive capacity. One explanation is that the results of these empirical studies are sensitive to the measures of absorptive capacity used. This paper contributes to the empirical literature by investigating average years of schooling and total factor productivity gap as measures of host country absorptive capacity in 38 developed and developing countries. Panel data regression equations are estimated using a cross-sectionally heteroskedastic and timewise autoregressive (CHTA) model. The paper has two main results. The first result does not support the hypothesis that the technology diffusion from MNEs has a positive impact on the productivity growth in developing countries. The second result is that the total factor productivity gap is more appropriate than average years of schooling to measure host country absorptive capacity. This may suggest that the results of previous studies that used average years of schooling should be interpreted with caution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 0.001 |
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".