Lies and Statistics

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 to 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.

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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