Bibliographic record
Abstract
This paper analyzes earnings inequality and earnings dynamics in Sweden over 1985–2016. The deep recession in the early 1990s marks a historic turning point with a massive increase in earnings inequality and earnings volatility, and the impact of the recession and the recovery from it lasted for decades. In the aftermath of the recession, we find steady growth in real earnings across the entire distribution for men and women and decreasing inequality over more than 20 years. Earnings dynamics differ substantially by gender, education, and origin. Men face lower volatility than women, but their earnings growth is more closely tied to the business cycle. Earnings volatility is also higher among high-educated and foreign-born workers. We document an important role of social benefits usage for the overall trends and for differences across sub-populations. Higher benefits enrollment, especially for women and immigrants, is associated with higher earnings volatility. As the generosity and usage of benefit programs declined over time, we find stronger earnings growth among low-income workers, consistent with higher self-sufficiency. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".