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EconTalk

Gabriel Zucman on Inequality, Growth, and Distributional National Accounts

EconTalk

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4.74.3K Ratings

🗓️ 18 September 2017

⏱️ 73 minutes

🧾️ Download transcript

Summary

Gabriel Zucman of the University of California, Berkeley talks with EconTalk host Russ Roberts about his research on inequality and the distribution of income in the United States over the last 35 years. Zucman finds that there has been no change in income for the bottom half of the income distribution over this time period with large gains going to the top 1%. The conversation explores the robustness of this result to various assumptions and possible explanations for the findings.

Transcript

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0:00.0

Welcome to Econ Talk, part of the Library of Economics and Liberty.

0:09.3

I'm your host, Russ Roberts, of Stanford University's Hoover Institution.

0:13.8

Our website is econtalk.org where you can subscribe, comment on this podcast, and find

0:18.9

links and other information related to today's conversation.

0:21.7

We'll also find our archives where you can listen to every episode we've ever done going

0:26.1

back to 2006.

0:28.3

Our email address is mailadycontalk.org.

0:30.8

We'd love to hear from you.

0:34.7

Today is September 7, 2017.

0:36.9

My guest is Gabriel Suckman, professor of economics at the University of California

0:41.6

Berkeley.

0:42.6

He's the author of the Hidden Wealth of Nations, the Scrooge of Tax Havens, which was published

0:47.3

in 2015.

0:49.2

Our topic for today is his recent paper with Emmanuel Sayez and Thomas Piketty, Distribution

0:55.5

National Accounts, Methods, and Estimates for the United States, which has received a

1:00.1

great deal of attention in the popular press and among economists.

1:04.3

Gabriel, welcome to Econ Talk.

1:06.6

Thank you very much for having me.

1:08.2

So it's a very provocative and complicated paper.

1:11.9

We're going to try to do two things today, among a few others, but we're going to try to

1:16.0

give listeners an idea of what the paper finds and then how such a finding is actually

1:22.1

constructed using data.

...

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