Private information for foreign investment in emerging economies
Bibliographic record
Abstract
In previous studies it has been found that new foreign investment is significantly related to the stock of existing investment in the country/region. This paper's contribution is the finding that a Japanese firm's new investment in an emerging economy is positively correlated with its own previous investment in that economy and also with the current/planned investments by competitors. These two channels are primarily substitutes; that is, investment by competitors becomes less salient when the firm has experience in the market. The correlated behaviour is not explained by industrial agglomerations but appears to reflect the value of private information to investment in emerging economies. Information privée et investissement étranger dans les économies en émergence. Des travaux antérieurs ont montré que les nouveaux investissements à l'étranger sont co‐reliés de manière significative à la taille du stock d'investissement étranger dans le pays ou la région. La contribution de ce mémoire porte sur le fait qu'un nouvel investissement d'une entreprise japonaise dans une économie en émergence est co‐relié positivement avec son propre niveau antérieur d'investissement dans cette économie mais aussi avec le niveau des investissements présents et anticipés de ses concurrents. Ces deux canaux d'information sont des substituts: le niveau d'investissement par les concurrents devient moins important à proportion que l'entreprise a acquis de l'expérience dans ce marché. Ce comportement co‐relié n'est pas expliqué par les effets d'agglomération industrielle mais semble refléter la valeur de l'information privée quand on investit dans des économies en émergence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".