Bibliographic record
Abstract
This thesis presents three studies in economic growth. The first chapter (with Marina Tavares) examines a novel channel through which financial shocks are transmitted across countries, the international capital markets of multinationals. We exploit empirical variation in the availability of credit to U.S. multinationals during the 2008-09 financial crisis to identify the strength of this channel. We estimate that the U.S. credit crunch accounted for a quarter of the drop in sales of European subsidiaries during the crisis. Through the lens of a dynamic multicountry model, our micro estimates imply that financial linkages within multinationals account for at most a fifth of global GDP comovement. Our findings suggest that countries with the largest foreign multinational presence experience more spillovers from foreign financial shocks, especially from shocks originating in the United States. The second chapter proposes a tractable quantitative framework to examine the role of inter-industry productivity spillovers in the catch-up growth of open economies. First, I document that a country’s comparative advantage tends to increase in industries that employ occupations that are used most intensively in current exports. The model rationalizes these patterns by incorporating occupation-specific dynamic scale economies into a multi-sector gravity framework. I estimate that scale economies are relatively large in high-skilled production. Simulations suggest that spillovers play a quantitatively substantial role in accounting for slow cross-country convergence and increase the gains from trade, especially in countries with a comparative advantage in manufacturing. The third chapter (with Swapnika Rachapalli and Diego Restuccia) uses variation in land-market institutions across Indian states and detailed micro panel data to study distortions in land rental markets and their impact on agricultural productivity. We find empirical evidence that states with more rental-market activity feature less misallocation and over time reallocate land more efficiently. We develop a model of land rentals across heterogeneous farms to estimate land-market distortions in each state and assess their quantitative effect on agricultural productivity. Land rentals have substantial positive effects on agricultural productivity: an efficient reallocation of land increases agricultural productivity by 38 percent on average and by more than 50 percent in states with highly distorted rental markets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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".