Eliminating the Pass-Through: Towards FDI Statistics that Better Capture the Financial and Economic Linkages between Countries
Bibliographic record
Abstract
FDI plays a central role in managing global production networks, but FDI statistics also reflect other factors, including tax avoidance, that make it difficult to differentiate between FDI for “long-term” investments that serves as a source of growth and FDI that is purely financial and has little real economic impact as it merely passes through an economy. This latter FDI also obscures the ultimate sources and destinations of FDI. This paper addresses these challenges by developing a framework for consolidated FDI statistics based on the nationality of the MNE group that complements residency-based FDI statistics. While residency-based statistics are useful to identify where financial claims and liabilities are held, nationality-based statistics provide information on who makes the decisions, reaps the benefits, and bears the risk. Consolidated FDI statistics remove pass-through capital and are better for understanding ‘real’ financial integration between economies and for analysing the relationship between the financing of MNEs and their operations. While some countries produce separate FDI statistics for resident Special Purpose Entities (SPEs) to identify pass-through capital, we demonstrate that this only provides a partial view and that about one-quarter of the inward FDI positions in a selection of European countries reflects pass-through capital through non-SPEs.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".