Household Income is Extremely Misleading

Household income trends are often cited as reason for “solutions” to income inequality. As a raw statistic, HHI is very misleading because it fails to account for individual behavior in any given economy. Let’s do a thought experiment to explain why, and then look at a more accurate measure of income distribution.
We have a household with three roommates. All three roommates have an individual income of $100 per year, thus a household income of $300. This kind of living situation is not uncommon, specifically with younger people.

Let’s say all three roommates receive a raise of $50, which is enough to cover living expenses without roommates. All three move out and relocate in three separate households. Now, we have three households with three separate incomes, all of which are less than the original $300 HHI. That said, is there any indication that either of them are less well off? Not at all.

Now let’s suppose we have another household – an older couple with presumably larger incomes. One person is the breadwinner and the other stays at home. Let’s say their HHI is $500 a year.

The couple, for whatever reason, gets a divorce and move out. Now we have one household (the breadwinner) with a $500 HHI and another household with a $0 HHI. Again, there is no indication that either party is less well off, aside from the divorce settlement. In fact, if there was a large settlement favoring the stay-at-home person, then that would still not be counted as HHI. That person could be living a comfortable lifestyle and, to the extent of HHI, have an income of $0. As divorce rates have jumped to 50%, this scenario becomes more common and influential in aggregate statistics.

Both instances skew statistics so much that it would appear that “average household income” has stagnated. Furthermore, data regarding household make up shows that the average number of people per household has declined from 3.33 in 1960 to 2.54 in 2013, meaning that a household’s income is supporting fewer household members. A similar point can be made with Census data that indicates an increase in single person households.

So how can we best measure income?

Economists Robert Haig and Henry Simons created a very broad definition of income. The Haig-Simon metric includes: wages/salaries, transfer payments (such as employer insurance), gifts of inheritance, income in-kind, and net increases in the real value of assets.

Many researchers at the National Borough of Economic Research agreed that the Haigs-Simons income definition is an “attractive standard for calculating income”. They found that employing Haigs-Simon “dramatically reduces the observed growth in income inequality across the distribution, but most especially the rise in top-end income since 1989”. In fact, they concluded that, with the Haigs-Simon metric, “top income shares are volatile, but have not significantly increased over the last 20 years” and most of the income gains were in the bottom 80%. This can be largely attributed to accounting for capital gains in the year they were accrued, as opposed to when they are realized via IRS data. Although we have not discussed it specifically, Haig-Simon also happens to be a critical part of debunking the work of Thomas Piketty and Emmanuel Saez regarding income inequality.

Another improved metric is called “equivalised household income,” which seeks to correct the aforementioned household-member problem, by weighing household members differently to control for cross comparison discrepancies.

Many income distribution methods, such as average or median household income, fail to account for individual trends in an economy, do not adjust for changes in household makeup, and therefore don’t provide a fully inclusive or accurate snapshot of income. By including all forms of income and accounting for the variables described above, one can observe the truth about incomes and inequality. For one, incomes haven’t actually stagnated over the last few decades, as so many have inaccurately suggested, and two, income inequality is not increasing at some alarming rate, as many would have you believe.

Advertisements

2 comments

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s