Re-estimation of the savings retention coefficient in OECD countries: a new measure of home country bias
Bibliographic record
Abstract
Abstract The recent home bias is primarily related to the equity bias as measured by the international capital asset pricing model (CAPM); this measure has declined over the past two decades amid financial globalization, but remains high in most developed countries. The key to understanding this puzzling phenomenon lies in how accurately this indicator can be predicted. In this paper, we propose to use the savings retention coefficient estimated by the Feldstein–Horioka (F–H) regression as a cross-country measure of home bias. We re-estimate the savings retention coefficient based on an estimation model in which the FH regression is embedded in the model derived from Tobin's q-theory of saddle-road dynamics of investment under convex adjustment costs. We then use dynamic panel estimation to estimate the new measure in OECD countries. The main empirical results are as follows. The new measure of home country bias, such as the equity bias measure, declined steadily until 2008, but recovered to the level of the 60 s and 70 s after the 2008 financial crisis. Interestingly, people expected home country bias to be very high after the financial crisis, but in fact it simply returned to its previous level.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".