The Impacts of Changing Trade Policies, the Pandemic, and the Russia-Ukraine War on Trade Volumes, Jobs, and Economic Growth in Louisiana Port City Regions and the State of Louisiana
Bibliographic record
Abstract
On March 1, 2018, President Donald Trump signed an Executive Order that imposed a 25 percent tariff on steel and a 10 percent levy on aluminum. Citing national security concerns and Section 232 of the Trade Expansion Act of 1962, the President described the tariffs as an effort to “level the global economic playing field.” Over the next several months, he also imposed tariffs on other goods imported from China as well as ally nations, such as Mexico, Canada, and countries within the European Union. The U.S. is both a consumer of imported steel and aluminum and a major exporter of corn and soybeans. The steel and aluminum tariffs have had far-reaching effects, including retaliatory tariffs imposed by affected nations, and especially China, on U.S. agricultural products. The Lower Mississippi River Port Complex is the largest port complex in the world and a major international trade hub that is critical to the global supply chain system. Trade policies that impact the Louisiana ports can have far-reaching effects that go well beyond their immediate boundaries. The research carried out in support of this dissertation seeks to increase our understanding of how international trade policies can affect the productivity of ports and the economies of the communities that support them. It measures the cargo volumes through the ports following imposition of the steel and aluminum tariffs and the retaliatory tariffs on corn and soybean exports and measures the impacts of the trade policies on employment in the Transportation and Warehousing sector and the economic growth of the port city regions and the state of Louisiana. It also examines the impact on the ports of the COVID-19 pandemic and the Russia- Ukraine War; global events that occurred concurrently with imposition of the tariffs. Overall, the analysis found that the tariff policy had significant implications for trade dynamics, import volumes, local economic performance, and supply chains. The findings underscore the complex and evolving nature of international trade, which necessitates careful consideration of trade policies and their potential effects on local and regional economies and supply chain dynamics before they are enacted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".