The Economic and Political Legacy of Trump's First Term: Implications for the Second Presidency
Bibliographic record
Abstract
ABSTRACT This comprehensive study investigates the profound and enduring economic consequences of Donald Trump's 2016 election victory on the global economy, as observed through 2024. Utilizing a sophisticated mixed‐methods approach, the research combines rigorous quantitative analysis of economic indicators with qualitative assessment of policy shifts, providing a nuanced understanding of long‐term changes in international trade relations, financial markets, and socioeconomic dynamics over the past 8 years. The study's key findings reveal that Trump's “America First” policies and protectionist measures, including tariffs on China and the renegotiation of the North American Free Trade Agreement into the Agreement between the United States of America, the United Mexican States, and Canada, have had lasting effects on global trade patterns and supply chains. Quantitative analysis demonstrates that stock market volatility remains elevated by 10%–15% above pre‐Trump levels, while currencies in countries heavily dependent on United States (US) trade continue to be undervalued by 5%–7%. Foreign direct investment inflows to the US are observed to be 20% lower compared to 2016 levels, and domestic income inequality has increased by 15%. Furthermore, the research identifies significant shifts in global economic power dynamics, with emerging economies, particularly in Asia, gaining increased influence. The study notes a 25% increase in intra‐regional trade agreements outside of US influence, indicating a diversification of global economic partnerships. Additionally, the analysis reveals a 30% rise in investments in renewable energy sectors globally, partly in response to the US withdrawal from climate agreements during Trump's tenure. The study concludes that Trump's economic policies have induced structural changes in the global economy, accelerated the rebalancing of global economic powers, and fundamentally altered the nature of international economic relations. Related Articles Garrett Terence M. Sementelli Arthur J. 2023. “Revisiting the Policy Implications of COVID‐19, Asylum Seekers, and Migrants on the Mexico–U.S. Border: Creating (And Maintaining) States of Exception in the Trump and Biden Administrations.” Politics & Policy 51, 458 475. https://doi.org/10.1111/polp.12537 . Moynihan Donald. 2025. “Trump, Personalism, and US Administrative Capacity.” Politics & Policy 53, e70059. https://doi.org/10.1111/polp.70059 . Stockemer Daniel. 2025. “Is the US Moving Towards Autocracy? A Critical Assessment.” Politics & Policy 53, e70032. https://doi.org/10.1111/polp.70032 .
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".