Bibliographic record
Abstract
In an integrated world capital market with perfect information, all forms of capital flows are indistinguishable.Information frictions and incomplete risk sharing are important elements that needed to differentiate between equity and debt flows, and between different types of equities.This survey put together models of debt, FDI, Fpi flows to help explain the composition of capital flows.With information asymmetry between foreign and domestic investors, a country which finances its domestic investment through foreign debt or foreign equity portfolio issue, will inadequately augment its capital stock.Foreign direct investment flows, however, have the potential of generating an efficient level of domestic investment.In the presence of asymmetric information between sellers and buyers in the capital market, foreign direct investment is associated with higher liquidation costs due to the adverse selection.Thus, the exposure to liquidity shocks determines the volume of foreign direct investment flows relative to portfolio investment flows.In particular, the information-liquidity trade-off helps explain the composition of equity flows between developed and emerging countries, as well as the patterns of FDI flows during financial crises.The asymmetric information between domestic investors (as borrowers) and foreign investors (as lenders) with respect to investment allocation leads to moral hazard and thus generate an inadequate amount of borrowings.The moral hazard problem, coupled with limited enforcement, can explain why countries experience debt outflows in low income periods; in contrast to the predictions of the complete-market paradigm.Finally, we analyze a risk-diversification model, where bond holdings hedge real exchange rate risks, while equities hedge non-financial income fluctuations.An equity home bias emerges as a calibratable equilibrium outcome.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".