Essays on Trade Policy and Macroeconomic Stability
Bibliographic record
Abstract
This thesis examines the effects of government policies, such as tariffs at the industry and aggregate levels, and the macroeconomic impact of carbon taxes. The first chapter examines the welfare costs of tariffs. I develop a political economy model showing that tariffs are strategically targeted during trade wars. This selection affects the estimation of trade elasticities: revenue-motivated governments tax sectors with low demand elasticity, while retaliation targets high-elasticity goods to maximize harm. As trade policy focuses on the extremes of the elasticity distribution, Trump’s tariffs align with low estimates around 2.5, while Canada’s retaliation yields an upper-bound estimate of 5.2. With zero export supply elasticity, the welfare cost of tariffs could potentially be twice as high. The second chapter estimates the dynamic effects of import tariffs on key macroeconomic aggregates. Using data on temporary trade barriers, I show that these tariffs are countercyclical, inducing bias in the computation of impulse response functions. To address this, I develop a novel instrument based on retaliatory tariffs. Since retaliation responds to a partner’s action, it is less likely to correlate with domestic shocks. Moreover, under reciprocity, retaliatory tariffs mirror those imposed by trade partners. Using this instrument in a Proxy-SVAR, I find that tariffs have a highly contractionary effect. The third chapter, co-authored with Pablo Gutierrez Cubillos and Bastián Castro Nofal, evaluates the role of carbon taxes as automatic stabilizers in small open economies specializing in the export of a single commodity. We examine the carbon tax's ability to reduce the volatility of the real exchange rate and energy prices. Using a DSGE model calibrated to the Chilean economy, which is highly specialized in copper production, we find that the tax reduces energy and energy price volatility and lowers the variance of the real exchange rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".