The Transfer Problem Surfaces in Sub-Saharan Africa: Net Foreign Assets, Financial Liberalization and Real Exchange Rates
Bibliographic record
Abstract
This paper presents new links among net foreign assets (NFA), financial liberalization, and the real exchange rates in Sub-Saharan Africa (SSA), utilizing a testable theoretical model inspired by Lane and Milesi-Ferreti (2004) and newly constructed data sets for real exchange rates, net foreign assets, and financial liberalization. First, we check for the existence of a transfer problem – the hypothesis that increases in NFA strengthen the real exchange rates. Second, we examine how real exchange rates have reacted to financial liberalization in SSA. Finally, we explore whether financial liberalization dampens the effects of a transfer problem. Empirical analysis, using cross-country data, confirms the existence of a transfer problem that decreases with increases in trade openness in SSA. We also find that, overall, countries with financial liberalization have more depreciated real exchange rates and that financial liberalization dampens the transfer problem so that the semi-elasticity of NFA becomes negative, implying that financially liberalized SSA countries that experience an increase in net external liabilities would eventually require an appreciated, rather than depreciated, real exchange rate. The results are robust to various model specifications and estimation techniques, inclusion of other determinants of real exchange rates and consideration of endogeneity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".