Essays on Multinationals and International Spillovers.
Bibliographic record
Abstract
The essays in this dissertation examine the causes and consequences of international spillovers and globalization, with a focus on the role of multinational firms. Chapter 1, joint with Christoph E. Boehm and Aaron Flaaen, highlights the important role played by U.S. owned multinationals in the decline of U.S. manufacturing, using a new microdata panel from 1993-2011 augmented with multinational identifiers and transactions level trade. These multinationals are responsible for both 41% of the manufacturing employment decline and the majority of input imports over this period. A structural framework establishes that the supply-chain fragmentation associated with the imports of intermediates has been substituting for domestic employment. Our estimates imply that approximately half the observed employment decline can be attributed to the foreign sourcing of inputs by multinationals. Chapter 2, also with Christoph E. Boehm and Aaron Flaaen, changes focus to a cause of international spillovers -- the inflexible supply chains of multinationals. Using novel firm-level microdata and leveraging a natural experiment – the 2011 Tohoku Earthquake --, this paper demonstrates that the substantial intermediate input linkages displayed by foreign multinational affiliates in the U.S. with their source country are a source of the international transmission of shocks. The scope for these linkages to generate cross-country spillovers depends on the elasticity of substitution with respect to other inputs. Structural estimates of this elasticity are close to zero, suggesting that global supply chains are sufficiently rigid to play an important role in the cross-country transmission of shocks. Chapter 3, joint with Andrei A. Levchenko, examines the role of non-technology shocks in the international transmission of business cycles. We propose a novel identification scheme for a non-technology business cycle shock, that we label “sentiment.” This is a shock orthogonal to conventionally identified surprise and news TFP shocks. We estimate the international transmission of three identified shocks -- surprise TFP, news of future TFP, and “sentiment” -- from the US to Canada, and find that the sentiment shock is more important than either of the technology shocks in generating business cycle comovement in the short run.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".