Digitalization's divergent impact on FDI inflows: A comparative analysis between advanced and developing countries
Bibliographic record
Abstract
Digital technology in host countries serves as a significant comparative advantage in attracting FDI inflows. Does digitalization affect FDI inflows in different ways between advanced and developing countries? To unravel this question, we employ broadband subscriptions and Internet users as proxies for digitalization, examining their influence on FDI inflows across 37 advanced and 100 developing countries from 2002 through 2022. The two-step difference GMM Arellano-Bond and PMG estimators are applied to control endogeneity and serial autocorrelation. The findings present intriguing insights: First, digitalization and institutional quality emerge as magnets for FDI inflows in developing economies, while exerting deterrent effects in advanced countries. Second, market size increases FDI inflows in advanced economies, but decreases them in developing countries. Third, across advanced and developing countries, trade openness and infrastructure positively influence FDI inflows, whereas inflation is a hindrance. These revelations underscore essential policy implications for the governments of both advanced and developing countries. Policymakers may need to tailor strategies to harness the benefits of digitalization for FDI attraction, taking into account the distinct economic contexts and challenges faced by each group of countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".