The Impact of COVID-19 on Foreign Investors: Evidence from the Quarterly Global MNE Pulse Survey for the Third Quarter of 2020
Bibliographic record
Abstract
As the COVID-19 crisis extends into the second half of 2020, the outlook for both the pandemic and the associated economic crisis remains highly uncertain. In this environment, multinational enterprises (MNEs) need to weather a prolonged economic downturn while also navigating government policy responses to the pandemic and updating investment plans for an uncertain future. Given the importance of foreign direct investment (FDI) to the crisis and recovery, especially for developing countries, the World Bank Group’s Global Investment Climate Unit is conducting quarterly pulse surveys of MNE affiliates throughout 2020 to gauge the pandemic’s effect on foreign investors. According to previous rounds of the survey, four in five MNE affiliates experienced reduced revenue and profits, and three in four experienced a decline in supply chain reliability in the first quarter of 2020 (Saurav, Kusek, and Kuo, April 2020). The adverse impacts became near-universal in the second quarter of 2020, with over 90 percent of MNEs experiencing adverse effects (Saurav, Kusek, Kuo, and Viney, September 2020). A third round of the quarterly pulse survey, reflecting the third quarter of 2020, was administered in October and November 2020. The survey results show that the pandemic’s adverse effects remained widespread for MNE affiliates in the third quarter, with only limited improvements expected in the fourth quarter. While these survey results may not be generalizable to all developing countries, they are directionally indicative of MNEs’ experiences in developing 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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".