The Uneven Short-Run Effects of the COVID-19 Pandemic on Foreign Direct Investment
Bibliographic record
Abstract
This study examines short-run economic and business impacts of the COVID-19 pandemic as a global disruption event. The purpose is to build propositions about specific subnational FDI (Foreign Direct Investment) developments in the short-term of global disturbance. We approach the investigation by reviewing FDI outcomes in the year prior and in the first year of the pandemic, at the U.S. national and subnational levels, and through the lens of local characteristics and FDI outcomes in the state of New Hampshire. Our methods include distribution and frequency analyses on two sets of data: secondary data on FDI and trade at the state level, and primary data as direct observations on firm activities in New Hampshire. Our leading method is the evaluation of data aimed at triangulating and consequently generating a set of propositions that explain phenomena observed in relation to short-term effects of disruption. Our methodological tools consist of an in-focus instance of the phenomena in one state, as a particular case for verifying the validity of our propositions, and comparisons with available data across states to establish the reliability of the proposed consequences. Our analysis provides evidence for subnational heterogeneity of global disruption impact. Our interstate trend analysis and unique data on FDI-related activities in New Hampshire reveal how foreign businesses respond to the external shock of global disruption in the short-run. We use our insights to propose that established regional supply chains and differences in local advantages determine varying FDI outcomes across subnational locations. As a result, we set forth three calls-to-action for regional policymakers: the development of initiatives to support strong trade and FDI-outcomes at all times and in preparation for global disruption; the promotion and facilitation of firms’ access to markets; and the implementation of actions that encourage the establishment of regional supply chains.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".