Institutions, Protectionism, and Multinational Enterprises: Insights from Developing Economies
Bibliographic record
Abstract
This dissertation lies at the intersection of international trade policy, international business, and political economy, focusing on developing economies. Guided by theory, I leverage rigorous empirical analysis to derive actionable insights and convey evidence-based policy and management recommendations. My first line of research delves into institutional quality as a factor for Foreign Direct Investment (FDI). I address this in my chapter Reputational Shocks and Commitment Devices: Differential Effects on Foreign Direct Investment in Developing Economies, where I evaluate how reputational shocks affect FDI inflows in developing economies in the context of Investor-State Dispute Settlement. My second line of research focuses on protectionism and economic tensions between China and the United States in a strategic industry. In my third chapter, Effects of Trade Barriers on Foreign Direct Investment: Evidence From Chinese Solar Panels, I find the effects of US anti-dumping and countervailing duties on FDI decisions by targeted Chinese firms in the solar panel industry. I follow up on chapter four, Unraveling Protectionism: Strategic Responses of Chinese Multinationals to US Trade Policy, documenting the financial impact of these trade barriers on the whole corporate family containing a targeted Chinese firm in the solar panel industry, and the strategies they develop as a response. My research contributes to understanding how political economy and geopolitical factors shape international markets and impact developing economies and multinational enterprises in unstable global environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".