Arbeitspapier
Pareto models, top incomes, and recent trends in UK income inequality
I determine UK income inequality levels and trends by combining inequality estimates from tax return data (for the "rich") and household survey data (for the "non-rich"), taking advantage of the better coverage of top incomes in tax return data (which I demonstrate) and creating income variables in the survey data with the same definitions as in the tax data to enhance comparability. For top income recipients, I estimate inequality and mean income by fitting Pareto models to the tax data, examining specification issues in depth, notably whether to use Pareto I or Pareto II (generalised Pareto) models, and the choice of income threshold above which the Pareto models apply. The preferred specification is a Pareto II model with a threshold set at the 99th or 95th percentile (depending on year). Conclusions about aggregate UK inequality trends since the mid-1990s are robust to the way in which tax data are employed. The Gini coefficient for gross individual income rose by around 7% or 8% between 1996/97 and 2007/08, with most of the increase occurring after 2003/04. The corresponding estimate based wholly on the survey data is around -5%.
- Sprache
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Englisch
- Erschienen in
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Series: ISER Working Paper Series ; No. 2016-07
- Klassifikation
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Wirtschaft
Specific Distributions; Specific Statistics
Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
Personal Income, Wealth, and Their Distributions
- Thema
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inequality
top incomes
Pareto distribution
generalized Pareto distribution
survey under-coverage
HBAI
SPI
- Ereignis
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Geistige Schöpfung
- (wer)
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Jenkins, Stephen P.
- Ereignis
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Veröffentlichung
- (wer)
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University of Essex, Institute for Social and Economic Research (ISER)
- (wo)
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Colchester
- (wann)
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2016
- Handle
- Letzte Aktualisierung
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20.09.2024, 08:23 MESZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Jenkins, Stephen P.
- University of Essex, Institute for Social and Economic Research (ISER)
Entstanden
- 2016