Falling rates, rising partisanship effect: why market competition becomes associated with left governments in an era of low interest rates
Bibliographic record
Abstract
Abstract Scholars argue that while left partisan governments traditionally support stronger market regulation, this partisanship effect has started to vanish as left governments converge with the right in supporting deregulation, resulting in higher inequality. This paper argues that, instead of vanishing, the partisanship effect has intensified , but in a novel direction: left governments have become stronger defenders of market competition than other partisan governments . Furthermore, this new association between left partisanship and market competition has delivered new distributive gains for labor. I highlight the depressed interest rates across the rich world today in driving this outcome: low rates spark a rise in market concentration, which puts downward pressure on the labor share of income. By boosting market competition, left governments can counter this force and defend the labor share of income, thus revitalizing redistribution for a more difficult economic era. These claims are tested using data from 10 to 17 Organization for Economic Cooperation and Development (OECD) countries (1995–2017).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".