Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

As the authors write, “The U.S. is better described as a collection of societies, some of which are ‘lands of opportunity’ with high rates of mobility across generations, and others in which few children escape poverty.”


So what is it about parts of the United States where there is high income mobility? What makes some places better at equaling the playing field, of allowing a poor kid to have a pretty good life? Areas that spend more on education provide a better chance to poor kids. Places with more religious people and lower crime do better. Places with more black people do worse. Interestingly, this has an effect on not just the black kids but on the white kids living there as well. Places with lots of single mothers do worse. This effect too holds not just for kids of single mothers but for kids of married parents living in places with lots of single mothers. Some of these results suggest that a poor kid’s peers matter. If his friends have a difficult background and little opportunity, he may struggle more to escape poverty.

The data tells us that some parts of America are better at giving kids a chance to escape poverty. So what places are best at giving people a chance to escape the grim reaper?


We like to think of death as the great equalizer. Nobody, after all, can avoid it. Not the pauper nor the king, the homeless man nor Mark Zuckerberg. Everybody dies.

But if the wealthy can’t avoid death, data tells us that they can now delay it. American women in the top 1 percent of income live, on average, ten years longer than American women in the bottom 1 percent of income. For men, the gap is fifteen years.

How do these patterns vary in different parts of the United States? Does your life expectancy vary based on where you live? Is this variation different for rich and poor people? Again, by zooming in on geography, Raj Chetty’s team found the answers.

Interestingly, for the wealthiest Americans, life expectancy is hardly affected by where they live. If you have excesses of money, you can expect to make it roughly eighty-nine years as a woman and about eighty-seven years as a man. Rich people everywhere tend to develop healthier habits—on average, they exercise more, eat better, smoke less, and are less likely to suffer from obesity. Rich people can afford the treadmill, the organic avocados, the yoga classes. And they can buy these things in any corner of the United States.

For the poor, the story is different. For the poorest Americans, life expectancy varies tremendously depending on where they live. In fact, living in the right place can add five years to a poor person’s life expectancy.

So why do some places seem to allow the impoverished to live so much longer? What attributes do cities where poor people live the longest share?

Here are four attributes of a city—three of them do not correlate with poor people’s life expectancy, and one of them does. See if you can guess which one matters.

WHAT MAKES POOR PEOPLE IN A CITY LIVE MUCH LONGER?



The city has a high level of religiosity.

The city has low levels of pollution.

The city has a higher percentage of residents covered by health insurance.

A lot of rich people live in the city.



The first three—religion, environment, and health insurance—do not correlate with longer life spans for the poor. The variable that does matter, according to Chetty and the others who worked on this study? How many rich people live in a city. More rich people in a city means the poor there live longer. Poor people in New York City, for example, live a lot longer than poor people in Detroit.

Why is the presence of rich people such a powerful predictor of poor people’s life expectancy? One hypothesis—and this is speculative—was put forth by David Cutler, one of the authors of the study and one of my advisors. Contagious behavior may be driving some of this.

There is a large amount of research showing that habits are contagious. So poor people living near rich people may pick up a lot of their habits. Some of these habits—say, pretentious vocabulary—aren’t likely to affect one’s health. Others—working out—will definitely have a positive impact. Indeed, poor people living near rich people exercise more, smoke less, and are less likely to suffer from obesity.


My personal favorite study by Raj Chetty’s team, which had access to that massive collection of IRS data, was their inquiry into why some people cheat on their taxes while others do not. Explaining this study is a bit more complicated.

The key is knowing that there is an easy way for self-employed people with one child to maximize the money they receive from the government. If you report that you had taxable income of exactly $9,000 in a given year, the government will write you a check for $1,377—that amount represents the Earned Income Tax Credit, a grant to supplement the earnings of the working poor, minus your payroll taxes. Report any more than that, and your payroll taxes will go up. Report any less than that, and the Earned Income Tax Credit drops. A taxable income of $9,000 is the sweet spot.

And, wouldn’t you know it, $9,000 is the most common taxable income reported by self-employed people with one child.

Did these Americans adjust their work schedules to make sure they earned the perfect income? Nope. When these workers were randomly audited—a very rare occurrence—it was almost always found that they made nowhere near $9,000—they earned either substantially less or substantially more.

In other words, they cheated on their taxes by pretending they made the amount that would give them the fattest check from the government.

So how typical was this type of tax fraud and who among the self-employed with one child was most likely to commit it? It turns out, Chetty and colleagues reported, that there were huge differences across the United States in how common this type of cheating was. In Miami, among people in this category, an astonishing 30 percent reported they made $9,000. In Philadelphia, just 2 percent did.

What predicts who is going to cheat? What is it about places that have the greater number of cheaters and those that have lower numbers? We can correlate rates of cheating with other city-level demographics and it turns out that there are two strong predictors: a high concentration of people in the area qualifying for the Earned Income Tax Credit and a high concentration of tax professionals in the neighborhood.

What do these factors indicate? Chetty and the authors had an explanation. The key motivator for cheating on your taxes in this manner was information.

Most self-employed one-kid taxpayers simply did not know that the magic number for getting a big fat check from the government was $9,000. But living near others who might—either their neighbors or tax assisters—dramatically increased the odds that they would learn about it.

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