Valuation Effects and the Dynamics of Net External Assets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The traditional current account can be an inaccurate measure of the change in the net foreign asset (NFA) position. Using gross asset and liability positions at the country level, a number of 'valuation effects' have been identified which contribute to changes in NFA but do not enter the reported current account. This paper uses new developments in the analysis of portfolio allocation in general equilibrium to investigate valuation effects in a two-country model. The model can be used to analyze both qualitatively and quantitatively the role of valuation effects. Broadly speaking, the valuation effects in the model correspond to those in the data, and have the effect of enhancing cross country risk sharing. But there is a key distinction between "unanticipated" and "anticipated" valuation effects. Unanticipated effects can be large, dominating the movement in NFA, but anticipated effects arise only at higher orders of approximation and are small for reasonable parameterisations. The paper also analyses the determinants of international portfolio positions, and their role in generating valuation effects from asset price and terms of trade changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it