Three essays in International Economics
Bibliographic record
Abstract
My thesis consists of three essays on International Economics. In the first two chapters, I study the role of domestic markets on the issuance of sovereign debt. In the third chapter, I evaluate the aggregate consequences of large devaluations on exporting dynamics.\nChapter 2 studies the episodes of sovereign default triggering banking crises. First, those episodes are characterized by an important exposure of domestic banks to government bonds. Therefore, a default triggers a credit crunch in the economy. Second, output and investment show a considerable drop and a protracted recovery after the default. In this chapter, I focus on the channel in which a default affects the capital accumulation financed by banks. I build a quantitative model to assess whether this channel can account for the joint dynamics of output and investment during empirical episodes where a default preceded a banking crisis. The model is calibrated to reproduce the empirical moments of banks’ bondholding exposure and capital accumulation ex-ante to default in those economies that experienced a joint default and banking crisis. The model reproduces the untargeted dynamics of macroeconomic and financial variables during a default. Finally, I use the model to evaluate the ex-ante trade-off when a government issue government debt to banks. By issuing more debt, they decrease the incentives to default but it crowds-out capital accumulation in the economy. Also, I show that the crowding-out effect is lower for economies with a banking system that has more access to deposits.\nChapter 3 analyzes the trade-offs that a government faces when deciding whether to issue debt through domestic and foreign markets under limited commitment. I find empirical evidence showing a negative correlation between the exposure of domestic banks to government bondholdings with the interest rate spread compensating for the risk of default. In order to rationalize this fact, I develop a quantitative model where government chooses the optimal amount of debt issued to international investors and domestic banks and cannot discriminate across investors. The stock of debt held by foreign investor’s vis-a-vis the stock held by banks is meaningful to determine default incentives. While a repudiation of debt decreases the amount that should be paid by foreign lenders, the default is costly for domestic private intermediation. As a result, bond price worsens with the increase of foreign debt, while it improves with domestic debt. I parametrize the model the resemble the banking sector exposure to government bonds and default frequency of an emerging economy. The model is close to reproduce the untargeted share of domestic to foreign debt observed in the data. i Chapter 4 studies the macroeconomic effects of large and persistent devaluations. In the data we observe that after a large devaluation, net exports have a sluggish reaction and start to increase after several years. I show that this pattern characterizes the devaluation observed in real exchange rate of Colombia in 2014. I build a quantitative model of a small open economy featuring costs for incumbent and new exporters. A share of the cost is paid by importing goods from the rest of the world. Therefore, in this stylize model, I introduce the feature that exporters are also intensive importers. This resembles a feature of the Colombian data where firms that account for 90 % of the exports are also intensive importers. Both elements affects the dynamics of the extensive margin for exporters. I solve the model and reproduce a large devaluation event that matches the dynamics observed in the data for real exchange rate and interest rates. I show that the model is able reproduce the sluggish reaction of net exports. The model is calibrated to match the steady state of several moments of the Colombian economy for the period of 1980-2012. In particular, I calibrate the parameters dominating probability distribution of the continuation and sunk costs to match the continuing rate of exporters and the exit rate. I solve the model using local projection methods. I use the solution of the model to find the optimal path of shocks to resemble a RER devaluation and the increase in international interest rate. I show that the model is able to match the dynamics of the elasticity of exports to RER.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".