Is Foreign Direct Investment Resilient Post the COVID-19 Pandemic? The Case of a Subnational Economy
Bibliographic record
Abstract
The disruption brought about by the COVID-19 pandemic has been unprecedented in its global reach and unique impacts. While the literature has addressed the disruption effect on FDI at the country level, we provide a unique dive into the presence and development of FDI at a subnational location. We use detailed data on spatial and industrial distributions of FDI in the U.S. state of New Hampshire and find support for all our hypotheses related to post-disruption recovery and resilience. Given the varied impact of the pandemic on FDI across locations, and the heterogeneity in local conditions, we contend that the subnational recovery depends on the impact of the disruption and happens at varying levels and timelines. While the literature documented that foreign businesses choose to embed in their local host environments, few studies have considered empirically how the level of local integration affects FDI recovery after disruption. We propose that subnational locations with a high level of integration maintain relative strength in FDI post-disruption. The COVID-19 pandemic disruption presents an opportunity to evaluate FDI resilience. We postulate that existing FDI and spatial agglomerations of FDI-related activities impact the post-disruption resilience of FDI at a subnational location. The analysis concludes on actionable insights for researchers and practitioners regarding how to navigate the FDI inflows and activities at their specific location.
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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 | 0.002 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".