The Impact of COVID-19 on Foreign Investors : Evidence from the Quarterly Global MNE Pulse Survey for the Third Quarter of 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the COVID-19 crisis extends into the \n second half of 2020, the outlook for both the pandemic and \n the associated economic crisis remains highly uncertain. In \n this environment, multinational enterprises (MNEs) need to \n weather a prolonged economic downturn while also navigating \n government policy responses to the pandemic and updating \n investment plans for an uncertain future. Given the \n importance of foreign direct investment (FDI) to the crisis \n and recovery, especially for developing countries, the World \n Bank Group’s Global Investment Climate Unit is conducting \n quarterly pulse surveys of MNE affiliates throughout 2020 to \n gauge the pandemic’s effect on foreign investors. According \n to previous rounds of the survey, four in five MNE \n affiliates experienced reduced revenue and profits, and \n three in four experienced a decline in supply chain \n reliability in the first quarter of 2020 (Saurav, Kusek, and \n Kuo, April 2020). The adverse impacts became near-universal \n in the second quarter of 2020, with over 90 percent of MNEs \n experiencing adverse effects (Saurav, Kusek, Kuo, and Viney, \n September 2020). A third round of the quarterly pulse \n survey, reflecting the third quarter of 2020, was \n administered in October and November 2020. The survey \n results show that the pandemic’s adverse effects remained \n widespread for MNE affiliates in the third quarter, with \n only limited improvements expected in the fourth quarter. \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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.018 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it