Diaspora investments in low & high interest rate environments
Bibliographic record
Abstract
Diaspora investment flows measured as foreign direct investments represent one of the major outcomes of the activities of diaspora investors, entrepreneurs, and venture capitalists in the economy. This paper contributes to the literature with distinct analysis of diaspora investment flows in low interest rate environments (Canada, Denmark, Euro area, Japan, Korea Republic, Sweden and the US) and high interest rate environments (Brazil, China, Colombia, India, Indonesia, Mexico and Turkey). First, we employ the Bounds cointegration analysis to investigate whether diaspora investment integrates either of the two groups of economies. Second, we apply the Toda-Yamamoto causality approach to examine whether the interest rate environment causes diaspora investment inflows. Third, we employ the Autoregressive Distributed Lag-Mixed Data Sampling (ADL-MIDAS) technique to evaluate the role of macroeconomic performance for attracting diaspora investments. We find proof of financial integration of diaspora investments in all the low interest rate economies, whereas the evidence is limited to three countries in the high interest rate environment. We also find that the low interest rate environment (more than the high interest rate environment) engenders diaspora investment inflows and also enhances the positive impact of macroeconomic performance in attracting diaspora investments. We highlight some insightful investment and policy implications from the findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".