Twenty-five years of income inequality in Britain: the role of wages, household earnings and redistribution
Bibliographic record
Abstract
We study earnings and income inequality in Britain over the 25 years prior to the COVID-19 pandemic. We focus on the middle 90% of the income distribution, within which the gap between top and bottom in 2019–20 was essentially the same as a quarter-century earlier. We show that this apparent stasis is in fact the net effect of various mutually offsetting changes which are important in their own right. The proportion of working-age households with no one in paid work has been falling for most of the period, reducing inequalities in household labour income across the working-age population. Between the mid 1990s and the Great Recession, however, the gap in earnings between low-earning working households and higher-earning working households was rising, due in part to an increasing tendency for low-wage men to work part-time. But increasing fiscal redistribution kept the gap in disposable income between those same households roughly constant, while also closing the gap between the incomes of workless households and the rest. Together with the falls in worklessness, this was sufficient to achieve some decline in income inequality across the middle 90% of the distribution. In the past decade, key trends turned around. Household earnings inequalities reversed direction, as hours of work for low-wage men stopped falling and hourly wage growth was strongly progressive for both men and women – in part due to a rising minimum wage. Yet household disposable income inequalities also reversed, in the opposite direction, due to large cuts to working-age income-related transfers.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".