Impact of trade liberalization and regional integration on firm behaviour, income levels, and industrialization
Bibliographic record
Abstract
This dissertation investigates the influence of economic integration through trade on several aspects of economic activity. The aim is to obtain empirical evidence on whether and how trade integration affects economic activity, and to provide theoretical frameworks to organise and understand the empirical findings. Since trade liberalization and regional integration agreements are the main tools through which the extent of integration can be influenced by policy makers, they are of particular interest throughout the analysis. In the first substantive chapter, I analyse the role of proximity to product markets in explaining the variation of regional per capita income in the European Union. Using a New Economic Geography model, I derive an econometric specification relating income levels to a trade cost and price index weighted sum of the surrounding locations' GDP. I estimate this specification for a sample of 193 EU regions for 1975-1997 and find an important role for market access. However, its main benefits seem to come from increased incentives for physical and human capital accumulation and not through direct trade cost savings. In the second chapter, I extend the analysis of the importance of geographic position and the extent of market integration to the developing world. In particular, I point out that economic geography plays an important role in explaining levels of industrialization in developing countries. First, good access to developed countries' product markets seems to be beneficial for industrialization. Second, geographic position also plays an important role in determining a country's comparative advantage and thus its sectoral specialization. Finally, the last chapter shifts the focus of analysis from the macro-to the micro-level by analysing the impact of trade liberalization on firm-level behaviour. Specifically, I use the Canada-U.S. Free Trade Agreement of 1989 as a natural experiment to show that trade liberalization leads to an increase in mergers and acquisitions activity. I also provide evidence that resources are transferred from less to more productive firms in the process and that the magnitude of the overall transfer is quantitatively important.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".