# On the Many Ways to Spin Statistics

From household income to capital gains, there are boundless possibilities for spinning numbers to score political points.

Mark Twain famously said that there were three kinds of lies: “lies, damned lies, and statistics.” Since this is an election year, we can expect to hear plenty of all three kinds.

Even if the statistics themselves are absolutely accurate, the words that describe what they are measuring can be grossly misleading.

Household income statistics are an obvious example. When we hear about how much more income the top 20 percent of households make, compared with the bottom 20 percent of households, one key fact is usually left out. There are millions more people in the top 20 percent of households than in the bottom 20 percent of households.

The number of households is the same but the number of people in those households is very different. In 2002, there were 40 million people in the bottom 20 percent of households and 69 million people in the top 20 percent.

A little over half of the households in the bottom 20 percent have nobody working. You don’t usually get a lot of income for doing nothing. In 2010, there were more people working full-time in the top 5 percent of households than in the bottom 20 percent.

Household income statistics can be very misleading in other ways. The number of people per household is different among different racial or ethnic groups, as well as from one income level to another, and it is different from one time period to another.

The number of people per American household has declined over the years. When you compare household incomes from a year when there were 6 people per household with a later year when there were 4 people per household, you are comparing apples and oranges.

Even if income per person increased 25 percent between those two years, average household income statistics will nevertheless show a decline. When the income of 4 people rises 25 percent, this means that 4 people are now making the same income as 5 people made in an earlier time. But not as much as 6 people made before.

So household income statistics can show an economic decline, even when per capita income has risen.

Why do so many people in the media, in academia and in politics use household income statistics, when the number of people per household can vary so much, while individual income statistics always mean the average income of one person?

Although individual income statistics can give a truer picture, not everyone makes truth their highest priority. Alarming news that household incomes have failed to rise, or have actually fallen, is more exciting news for the media, or for alarmists in academia or in politics.

Such alarming news can attract a larger audience for the media, and can justify an expansion of government programs dear to the heart of academics on the left, or to politicians who just want more power to hand out goodies and collect more votes from the beneficiaries.

Even individual income statistics have pitfalls when they lump together very different kinds of income, as is usually the case. Incomes from salaries are very different from incomes from capital gains.

A salary is usually earned and paid in the same year. Capital gains received in a given year can be paid for value accrued over a number of years. If you paid \$100,000 for a home or a business in the past, and then sold it 20 years later for \$300,000, have you made \$200,000 per year when you sold it or \$10,000 a year for 20 years?

In the income statistics, your income will be recorded the same as that of someone on a salary of \$200,000 a year.

What difference does that make? It makes a big difference when most low and moderate incomes are from salaries, while incomes in the highest brackets are more likely to be primarily capital gains — whether from the sale of homes or businesses, or receiving an inheritance, cashing in stock options, or some other forms of capital gains.

This means that statistics on income inequalities are often comparing high multi-year earnings with lower single-year earnings — that is, comparing apples and oranges.

Such statistical distortions are discussed more fully in my book Wealth, Poverty and Politics. In an election year, it might be worth taking a look.

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