Information Diffusion in International Markets
Bibliographic record
Abstract
Globalization has been a persistent phenomenon of the post-war period. The gross volume of cross-border capital flows has grown at an average of 25 percent a year, and trade in goods and services has also increased, albeit not as dramatically, but at least twice as fast as world GDP over the past 20 years. Yet, consumers and investors continue to spend and hold a disproportionate share of their assets in local markets-the so-called home-bias has been emphasized by many recent empirical studies. For many researchers, this home bias reflects information asymmetries and the fact that acquiring information across international borders is relatively costly. The main objective of the authors is to identify channels through which information gets disseminated across international markets. They consider three potential channels through which information can affect import and foreign equity purchase decisions in 14 OECD countries. The first channel consists of information spillovers from the commercial to the financial markets and vice-versa. Financial investors and importers share common information, which is also frequently conveyed to them by the same source-banks or financial intermediaries. The second and third channels emphasize seller and buyer reputations in international markets. The seller reputation channel stresses the importance given by, for example, importers in the United States who are considering buying products from Italy to the experience that Canadian and Japanese importers may have accumulated on Italian exporters. The buyer reputation channel examines to what extent a foreign investor or trader seeks information on the reliability of the foreign buyer by assessing his reputation in other countries. While the last two channels are equally important in explaining bilateral import flows, buyer reputation appears to be of greater importance for equity flows in the sample. The authors argue that these three channels may help provide some insights about the recent episodes of contagion across markets and countries that occurred over the past decade. These information channels can create virtuous or vicious circles that may, in turn, lead to unexpected changes in investors' and traders' behaviors across markets.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".