The Impact of COVID 19 on Foreign Investors : Evidence from the Quarterly Global Multinational Enterprise Pulse Survey for the First Quarter of 2021
Bibliographic record
Abstract
As the Coronavirus (COVID-19) pandemic \n enters its second year, foreign investors can see light at \n the end of the tunnel as economic conditions improve. \n Results from the latest round (Q1 2021) of World Bank \n Group’s quarterly pulse surveys of Multinational Enterprise \n (MNE) affiliates suggests that most firms are still \n operating below full capacity. The pandemic’s adverse \n effects on MNEs in developing countries continued to ease in \n the first quarter of 2021 (Q1 2021). Impacts remained \n widespread with 93 percent experiencing at least one adverse \n impact, but fewer firms report negative demand, output, \n revenue, and profit impacts (relative to Q1 2020) than in \n prior survey rounds. The average magnitude of these impacts \n was also more limited and continued to ease from Q4 2020. \n The longer-term outlook for foreign investment in developing \n countries remains subdued. Overall, 92 percent of firms \n report that their foreign parent had no plans to change \n (increase or decrease) the company’s level of investment \n over the next 1-3 years. Uncertainty about future demand is \n the key factor holding investment back, but policy and \n regulatory restrictions are also a factor for almost half of \n firms. Survey results confirmed that almost all MNE \n affiliates have increased their adoption of digital \n technologies for remote working, e-commerce, and supply \n chain management in response to the pandemic. Half of MNE \n affiliates also report increasing their focus on \n sustainability and decarbonization of products and services, \n with foreign parent companies again playing a critical role. \n While these survey results may not be generalizable to all \n developing countries, they are directionally indicative of \n 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 | 0.013 |
| 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) | 0.003 | 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".