2016/12/07 by Stephen P. Jenkins · 1 voice · 154 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Comparability #Econometrics #Economic Theory and Policy #Economic inequality #Economics #Gender, Labor, and Family Dynamics #Gini coefficient #Income distribution #Income inequality metrics #Income, Poverty, and Inequality #Inequality #Mathematics #Operations management #Pareto principle #Percentile #Statistics #Survey data collection
paper · pdf · doi:10.1111/ecca.12217
published in Economica 84(334), 261-289 (Wiley)
openalex publication_date 2016/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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 (generalized 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 individual gross income rose by around 7% or 8% between 1996/7 and 2007/8, with most of the increase occurring after 2003/4. The corresponding estimate based wholly on the survey data is around −5%.