The US Census has released its data about “Income, Poverty and Health Insurance Coverage.” So now, like a man staring at a double-rainbow, it’s time for everyone to start publishing articles asking ‘What does this meeeeean?!” Let’s take a look at the actual report and try and figure some of this out. (First three images are from the report, the other two are from elsewhere (cited in-image).
You can look at the year-to-year data for last year and see a slight uptick in incomes and slight down-tick in inequality. That’s cool and all, but not really useful. Trying to understand economic patterns on a year-to-year basis is the equivalent of trying to cook chili with only beans.
Looking at the pre- and post-recession median household income vs historical numbers and the 90s tech-bubble highs is a little more useful. 2012 to 2013 gave us a slight uptick to a median income of $51,900, this is almost as far below peak bubble income (~$5,000) as it is above 1967 income (~$8,000).
Clearly for the middle class, there has been no recovery. In fact, the near plateau of the post-Real Estate Bubble period for 50% of the population indicates a stagnation. There has been slight growth, but it is slow and likely slowing.
Comparatively the top 10% of the population has seen a sharper growth post-recession, though not yet a recovery and the top 5% have shown significant growth as an overall pattern since 2008, nearly making up their recession losses.
For comparison (over 46 years):
The lowest 10% received a growth of $2,500
The median 50% received a growth of $8,300.
The top 10% received a growth of $59,300.
The top 5% received a growth of $81,900.
The Gini Index measures relative income across the entire country. If it were at 0.0 everyone in the country would make exactly the same amount of money. The chart provided shows two measures.
– The Money Income measure is a a measure of income earned through any non-leverage measures (salary, investments, etc…) before taxes over American households.
– The Equivalence-adjusted measure attempts to average out that household income by number of residents. The argument being that because the a single person household’s earnings retain more value than multiple-person household earnings. So in this measure a single dude making $40,000 would be counted with a higher salary than a family of 3 making the same amount.
The US’s Gini measure, which can be generally accepted from the census data as falling between 46 and 50% puts it below the world Gini index (last available estimate: 2005, marked as 68%) but puts us in the same sector as large swaths of South America and southern Africa. For comparison (2011 numbers), our polite friends to the north in Canada have a Gini of 30-35% and the Nordic countries (like Finland) have 25-30% (that’s the best it gets).
But (surprise!) the US Census data fails to tell the whole inequality story.
The problem is that US Census measures fail to account for any income larger than $999,999. Why is this? Not sure, perhaps they ran out of numbers on their calculators? Whatever the reason, it means that the wealth of the top 1% (whose average income in 2012 was $1,318,200) is literally Off The Charts and the comparative inequality that has been the focus of years of Occupy protests isn’t accounted for.
Ooops.
As a ‘fun’ bonus note, we can see that the average earning power of women falls almost $11,000 less than men, for an inequality ratio of 78% between the sexes. That makes us 17% less unequal than in 1960 (yay?). Even less impressive is the real dollars closing of the gap to the tune of a whole $4,000 over 43 years. At this rate, we’ll hit pay equality in a bit over 93 years if we go by dollars! Oh joy. Take heart, if we assume the average yearly percentage gain instead, it will only take 55 years. So, in our lifetimes!
What else does the census data fail to take into account when applied to the real world? Well, real world value for one thing. Without going too much into the specifics of inflation (we can talk about that later), the rise in overall wealth of the top 10, 5 or 1% causes prices of everything else to go up, regardless of relative wealth at other areas of the US population. So while median salary may differ by a ‘mere’ ~$8,000, it’s actual value has decreased (in terms of your ability to purchase goods and housing with it).
This causes a significant decrease in the actual wealth of the US population medium-income on down because they are increasingly ‘leveraged’ (my favorite fancy economist word for In Debt’). As the debt ratio of the poorest families shows, the poorest among us are now in debt for over 130% of their assets. That means that the real income of these families should be (in my, perhaps overly liberal mind) -30% of the total. After all, that’s the real situation for those in the bottom 25% of the US earning population. An ever increasing amount of that is ‘installment’ debt, which means they’re responsible for paying it annually or monthly.
Even worse, the household debt effect that might show as mitigated over the last 3 years in various statistical analysis ISN’T. A significant majority of the decrease in household debt over the last three years comes from discharging that debt: either declaring bankruptcy or, more often, simply stopping the payments to the bank because the excess debt “became insurmountable.”
The last chart shows the debt/income ration layered over the types of assets owned by class. This one is 2010 numbers, so they are particularly dire. They’re likely better now, but this should give you a better idea of what the debt situation is across America. In 2010 the average American in the lower 80% of overall earnings (let’s say that accounts for the Upper Middle Class on down) had 160% debt on their earnings, so for every $1 they made, they went $1.60 into debt. As far as I can tell, this sort of thing isn’t accounted for at all in the Census inequality measures.
Perhaps it should be?
For bonus reading: research how the tax burden falls disproportionately on the middle class.
