Paul Krugman's Nostalgianomics: Economic Policies, Social Norms, and Income Inequality
Bibliographic record
Abstract
What accounts for the rise in income inequality since the 1970s? According to most economists, the answer lies in structural changes in the economy -- in particular, technological changes that have raised the demand for highly skilled workers and thereby boosted their pay. Opposing this prevailing view, however, is Princeton economist and New York Times columnist Paul Krugman, winner of the 2008 Nobel Prize in economics. According to Krugman and a group of like-minded scholars, structural explanations of inequality are inadequate. They argue instead that changes in economic policies and social norms have played a major role in the widening of the income distribution. Krugman and company have a point. For the quarter century or so after World War II, incomes were much more compressed than they are today. Since then, American society has experienced major changes in both political economy and cultural values. And both economic logic and empirical evidence provide reasons for concluding that those changes have helped to restrain low-end income growth while accelerating growth at the top of the income scale. However, Krugman and his colleagues offer a highly selective and misleading account of the relevant changes. Looking back at the early postwar decades, they cherry-pick the historical record in a way that allows them to portray that time as an enlightened period of well-designed economic policies and healthy social norms. Such a rosy-colored view of the past fails as objective historical analysis. Instead, it amounts to ideologically motivated nostalgia. Once those bygone policies and norms are seen in their totality, it should be clear that nostalgia for them is misplaced. The political economy of the early postwar decades, while it generated impressive results under the peculiar conditions of the time, is totally unsuited to serve as a model for 21st-century policymakers. And as to the social attitudes and values that undergirded that political economy, it is frankly astonishing that self-described progressives could find them attractive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".