When Market Fundamentalism and Industrial Policy Collide: The Tea Party and the US Export-Import Bank
Bibliographic record
Abstract
For most major economies, state-backed export credit is a core element of industrial policy and their strategies to boost exports and economic growth. Surprisingly, however, at a time when its competitors are increasing their use of this policy tool, state-backed export credit has become the subject of a hotly contested political battle in the US. As a result of opposition from the Tea Party, the US Export-Import Bank was forced to halt its lending operations for five months in 2015 and subsequently limited to financing only the smallest transactions. In this article, I show that the disruption of export credit is undermining the competitiveness of key US industrial sectors and encouraging the movement of advanced, high-value-added manufacturing overseas. The case of export credit therefore presents an important puzzle: Why is the US moving in the opposite direction of other states and taking steps that undermine its economic interests? I argue that the internal US attack on export credit is fueled by the prevailing market fundamentalist ideology that has obscured the role of an active state in fostering the US’s economic success. This article demonstrates how the rise of a powerful anti-state movement is hindering the ability of the US to conduct effective industrial policy and maintain its economic primacy in the face of growing global competitive pressures.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".