Covid-19 Pandemic and Outward Foreign Direct Investment: A Preliminary Note
Bibliographic record
Abstract
Abstract Social-distance policy of most governments and the pandemic impact of corona virus (COVID-19) on human health are expected to shutter international investment and business environment. However, there is little or no study to show the early empirical evidence on this relationship, most especially its impacts on FDI flows in the economies. This note provides a preliminary evidence of the impact of COVID-19 on FDI outflows. Our data cover cross-sectional first quarter, average data; between 1 January – 31 March, 2020 from 43 countries. Using Ordinary least square (OLS) and Quantile regressions, we document that there is a positive relationship between COVID-19 confirmed cases and FDI outflows. In addition, there is a positive impact of COVID-19 related confirmed deaths on FDI outflows across all quartiles estimations. This means that COVID-19 pandemic fuels the foreign direct investment outflows. The major causes could be the reduction in the ability of firms to invest due to a shortage in the number of skilled employees because they care for their health safety, a decline in corporate profits and increase in cost of finance. In addition, the propensities to invest have been widely affected negatively in most economies. These factors also become obvious when most economies experience a very high level of risk perception in financial market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".