COVID-19—A Black Swan for Foreign Direct Investment: Evidence from European Countries
Bibliographic record
Abstract
This study aims to reconsider the role of foreign direct investment determinants for European national development and to analyze the impacts of the pandemic situation caused by COVID-19. Foreign direct investment is a source of development; therefore, this study includes empirical applications, specifically the random effect model, for EU countries, during the pandemic period. This study provides some valuable conclusions regarding the changes caused by the main determinants of foreign direct investment, such as unemployment, interest rates, economic growth, inflation, and business confidence. Additionally, the proxies of COVID-19 are the number of cases and number of deaths, both appearing to positively contribute to FDI outflow, the former with a higher impact than the latter. Based on the availability of the data, this paper deals with 22 European Union countries for Q1, Q2, and Q3 of 2020. Data for all the chosen variables were not available for the fourth quarter (Q4); thus, this period was not considered, which constitutes a limitation of this study, but confirms the need for robust FDI inflows to support the sustainable post-pandemic development recovery of less-developed EU countries. As the need for external funding sources, i.e., FDI inflow, grows in times of crisis, governments should take suitable measures to uplift the confidence of socially responsible foreign investors during difficult times generated by black swan events. There is almost no detailed research regarding the impact of COVID-19 on FDI flows received by European Union countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".