Bibliographic record
Abstract
This thesis contains three papers exploring topics related to international trade and market power. In Chapter 1, I develop a quantitative model of international trade in which firms have endogenous market power in both product and labor markets that depends on their sizes in those markets. I use the model to evaluate the importance of accounting for oligopsony power on firm-level outcomes, welfare, the distribution of aggregate income, and the gains from trade. I calibrate the model to Indian plant-level data and import data for manufacturing sectors. I find that oligopsony causes small decreases in welfare but large decreases in aggregate real wages compared to perfect competition in labor markets. Trade increases large firms’ labor market power. Additionally, the welfare gains from trade are larger with oligopsony but the real wage gains from trade are smaller. Chapter 2, which is based on joint work with Stephen Ayerst, Faisal Ibrahim, and Swapnika Rachapalli, examines how technology embodied in traded goods generates spillovers across countries and sectors that increase aggregate growth. Using patent citation data from the United States, we construct a knowledge input output (IO) table and compare it to the production IO table. We use these IO structures and data on bilateral sector-level trade flows to measure embodied technology in imports. We show that increases in this measure are positively associated with increases in sectoral innovation expenditures. We build a quantitative model of firm-level innovation and trade with knowledge spillovers and calibrate it to trade and production data. We find that spillovers are an important contributor to growth, particularly in developing countries. Chapter 3 investigates how the strength of intellectual property rights affects firms’ make-or-buy decisions for specialized input purchases. I study a model of vertical integration and outsourcing in which a firm and an input supplier have asymmetric information about the supplier’s ability to use the firm’s knowledge capital outside of the relationship. I show that under outsourcing some relationships break down in equilibrium, which causes ex-post inefficiencies. When intellectual property rights are weak, increasing their strength may not reduce these inefficiencies unless the increase is large enough.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".