Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter?
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
This paper explores the impacts of health pandemics on foreign direct investment (FDI) using the new world pandemic uncertainty index (WPUI). We investigate the effects of pandemics, including COVID-19, on FDI based on a sample of 142 economies and sub-samples (incomes and regions) from 1996 to 2019. The two-step system Generalised Method of Moments estimation of linear dynamic panel-data model (DPDGMM) is used in this study. The estimation results are robust with the results of the two-step sequential (two-stage) estimation of linear panel-data models (SELPDM) and the two-step system Generalised Method of Moments estimation (BBGMM). The results show that health pandemics have negative impacts on FDI. Significantly, the uncertainty caused by pandemics creates adverse shocks on FDI net inflows in Asia-Pacific countries and emerging economies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it