TRADE IN POLARIZED AMERICA: THE BORDER EFFECT BETWEEN RED STATES AND BLUE STATES
Bibliographic record
Abstract
Political and cultural polarization in the United States is widely discussed, but does it relate to any economic disconnection among states? We estimate the “border” effect between Red and Blue states using the gravity equation with a nonlinear generalized method of moments estimator to simultaneously overcome the problems associated with endogeneity, cross‐state price differences, and zero‐trade flow. The border effect is robustly confirmed for the 2000s, while not so robustly detected for the 1990s. Notably, in 2007, the border reduces trade between Red and Blue states to approximately 75% of the trade within each set of states. This estimated border effect is much smaller than the United States–Canada national border effect estimated by Anderson and van Wincoop (2003), and by Feenstra (2002), yet is comparable to the border effect that Nitsch and Wolf (2009) find for the former West and East Germanies approximately 10 years after reunification. While the border effect in Germany after reunification is decreasing, the border effect between the Red and Blue states is emerging. We also find the border effect is more significant for consumption, rather than intermediate, goods. The border effect is an important indicator for a potential dismantling of the economic connectivity in the United States . ( JEL D72, F10, F15, R1)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".