Nathan Hendren

We had been working with data at the IRS.

We had been working with data at the IRS.

We had been working with data at the IRS.

Before this, I was working mainly with survey data sets, where if you had 10,000 people in your data set, you were quite excited. In this data, you're dealing with millions.

Before this, I was working mainly with survey data sets, where if you had 10,000 people in your data set, you were quite excited. In this data, you're dealing with millions.

Before this, I was working mainly with survey data sets, where if you had 10,000 people in your data set, you were quite excited. In this data, you're dealing with millions.

And what that allows you to do is really kind of put a sharp knife into your analysis and really uncover patterns.

And what that allows you to do is really kind of put a sharp knife into your analysis and really uncover patterns.

And what that allows you to do is really kind of put a sharp knife into your analysis and really uncover patterns.

Actually, I remember the day vividly.

Actually, I remember the day vividly.

Actually, I remember the day vividly.

So Raj and I were both working away at the Internal Revenue Service.

So Raj and I were both working away at the Internal Revenue Service.

So Raj and I were both working away at the Internal Revenue Service.

The longer a child spent in a neighborhood with higher rates of upper mobility, the higher their outcomes were in adulthood.

The longer a child spent in a neighborhood with higher rates of upper mobility, the higher their outcomes were in adulthood.

The longer a child spent in a neighborhood with higher rates of upper mobility, the higher their outcomes were in adulthood.

You'd actually see higher outcomes on average for the four-year-old relative to the eight-year-old.

You'd actually see higher outcomes on average for the four-year-old relative to the eight-year-old.