One of the interesting things about being a data-centric person is an appreciation for those who keep their data straight. \u00a0It’s right up there with appreciation for those who must straighten out data which has been obscured by others. \u00a0By considering an end result such as a “quintile” and then using inferences drawn at that stage as new sources, it is possible to convince oneself of many things which are not true.<\/p>\n
For example, the “average income for the quintile” (of US households by income quintile, 2010) is roughly $11K for the lowest quintile, and roughly $170K for the highest.<\/p>\n
Two facts will illuminate this seemingly impossible disparity. \u00a0First, the lowest quintile has 0.42 earners per household*, while the highest has 1.97. \u00a0Second, the age breakdown for the lowest is fairly flat, whereas the highest quintile household has a huge preponderance of 35-64 year olds. \u00a0Without breaking out a chart**, the percentage of 35-64 year olds in the highest is about double in the lowest.<\/p>\n
If we break household income down by earner count, 1.97\/0.42 = 4.69, and 4.69*11,034 = 51,746, so an income-earning person in the top quintile of households is earning three times as much as the lowest, not fifteen as the household figures suggest.<\/p>\n
Adjusting for age seems inviting, but I don’t know how to ensure that I am measuring the right thing, so I won’t.<\/p>\n
<\/p>\n
3. Roughly 3 out of 4 households in the top income quintile included individuals in their prime earning years between the ages of 35-64, compared to only 43.6 percent of household members in the bottom fifth who were in that age group.<\/p>\n
via Income inequality can be explained by household demographics \u00ab The Enterprise Blog<\/a>.<\/p><\/blockquote>\n
Recall that these are quintiles of income by households, not quintiles of individuals or measures of wealth. \u00a0Even if the “top one percent” is ridiculously wealthy, they are in this example mixed in with the rest of the top twenty percent. \u00a0 It would be interesting to see this broken down by “centiles”, or simple percent ranking. \u00a0100 buckets instead of five, for a much more fine-grained analysis.<\/p>\n
At any rate, if there is some rampant income inequality, it is not to be found in the quintiles, as a look at the demographics quickly reveals. \u00a0There is an argument to be made that income influences demographics, in that the rich have fewer children (so I’m told), and grandpa is probably not living there in the house, but I’d wager that the influence of demographics on income is even greater.<\/p>\n
*\u00a0(say that in five single-person houses, only two have jobs, and there’s a 0.40)<\/p>\n
** The chart is available at the article linked in the quote. \u00a0I’ll stash a copy on this site in case the other should become unavailable, so let me know if the link goes dead.<\/p>\n
<\/p>\n
\r\n\r\n \r\n <\/i>\r\n <\/a>\r\n <\/span>\r\n<\/div>\r\n \r\n <\/i>\r\n <\/a>\r\n <\/span>\r\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"One of the interesting things about being a data-centric person is an appreciation for those who keep their data straight. \u00a0It’s right up there with appreciation for those who must straighten out data which has been obscured by others. \u00a0By considering an end result such as a “quintile” and then using inferences drawn at that stage as new sources, it is possible to convince oneself of many things which are not true.<\/p>\n
For example, the “average income for the quintile” (of US households by income quintile, 2010) is roughly $11K for the lowest quintile, and roughly $170K for the highest.<\/p>\n
Two facts will illuminate this seemingly impossible disparity. \u00a0First, the lowest quintile has 0.42 earners per household*, while the highest has 1.97. \u00a0Second, the age breakdown for the … Continue reading