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The Gini coefficient is based on the sum of pairwise income differences, which can be decomposed into separate sums for individuals. Differences vis-à-vis poorer people represent an individual's advantage, while those with respect to richer people constitute deprivation. Weighting deprivation and advantage differently produces a family of personal Gini coefficients whose population averages each equal the overall Gini coefficient. Properties of the personal indexes explain why the Gini coefficient is most sensitive to changes in the middle of typical income distributions. Behavior of the personal indexes also throws light on the inequality impacts of secular changes in income distribution. In a simple Kuznetstype process, the Gini coefficient first rises and then falls but, throughout, a personal Gini coefficient will be rising for people in the traditional sector, while it is falling for those in the modern sector. In a leading case, the population shifts associated with polarization in labor markets in advanced economies also reduce personal inequality at the top and increase it at the bottom. The shift of population toward the two extremes unambiguously raises personal inequality for those in the middle. The wage changes accompanying polarization can, however, reverse these results, particularly at the top, as illustrated by calculations for U.S. polarization between 1980 and 2005.

The cross-country relationship between Covid-19 crude mortality rates and previously measured income inequality and poverty in the pandemic’s first wave is studied, controlling for other underlying factors, in a sample of 141 countries. An older population, fewer hospital beds, lack of universal BCG (tuberculosis) vaccination, and greater urbanization are associated with higher mortality. The death rate has a consistent strong positive relationship with the Gini coefficient for income. Poverty as measured by the $1.90 per day standard has a small negative association with death rates. The elasticity of Covid-19 deaths with respect to the Gini coefficient, evaluated at sample means, is 0.9. Assuming the observed empirical relationships unchanged, if the Gini coefficient in all countries where it is above the OECD median was instead at that median, 67,000 fewer deaths would have been expected after 150 days of the pandemic - - a reduction of 10%. Shrinking “excess Gini’s” down to the G7 median reduces predicted deaths by 88,500, or 14%.

There is a gap in estimates of the personal distribution of wealth in Canada between 1902 and 1970. That gap is partly filled here, using estate multiplier estimates for 1946-1970 and survey results for 1970-2012. Estate multiplier estimates are adjusted for differential mortality, and the survey upper tails are adjusted in line with respected journalistic "rich lists". Top wealth shares decline from 1902 to 1970, similar to other advanced western countries. There is no trend since 1970, which contrasts with a rise in the United States. Reasons for this difference between Canada and the U.S. are considered.