1/OK, data mystery time.

This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE

That's weird. Income inequality in China is legendary.

Let's check this number.

2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".

The report is available here: https://t.co/mrvWz1IzIe
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.

The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.

Here's the Gini index: https://t.co/MvylQzpX6A

It looks as if the latest estimate for China's Gini is 42.2.

That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.

I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
6/FRED, which gets its Gini estimates from the World Bank, shows the same numbers: https://t.co/1y911qazo9

Everyone except the "Fair Progress?" report, and the New York Times feature, seems to agree that the World Bank's most recent estimate of China's Gini is 42.2.
7/It appears that China's own estimate of its Gini was 46.5 in 2016: https://t.co/dG58kH3LiS
8/So where the heck is the "Fair Progress?" report getting its super-low China Gini number? It seems like it's NOT from the World Bank's World Development Indicators, which is what the report cites.
9/I notice that in the "Fair Progress?" report cited by the NYT, the U.S. Gini is also a bit fishy. It's less than 40, when the World Development Indicators say it's a bit over 40.
10/The only other source the "Fair Progress?" report cites is the World Bank's Global Database on Intergenerational Mobility: https://t.co/95RnPYxMsB

But the GDIM doesn't have income GINIs. So that can't be where these weird numbers were from (unless the data was mislabeled).
11/Anyway I've been searching high and low for where the "Fair Progress?" report and the NYT got these weird Gini numbers, and I just can't find it. If anyone else can help me find where this comes from, I'd appreciate it.
12/As of right now, it's looking like the New York Times used some bad data for an incredibly widely read report, thus convincing a ton of people (incorrectly) that China is a far more economically equal place than the United States.

https://t.co/vmzz57YeFf
13/But if someone finds a reliable source for these Gini numbers, then please let me know!

(end...for now)
14/UPDATE: The mystery has been solved! https://t.co/Qw9aB7Qg9D

The Gini number the NYT used was from the 1980s. It was not labeled as such.
15/The people who wrote the New York Times story appeared not to realize this. Here's the caption and graph from their piece:
16/The NYT seems to have just made a mistake, and should change the text and the graph to reflect that these numbers are from the 1980s, not current.

(end)

More from Noah Smith

This thread demonstrates that a lot of academic writing that *looks* like utter nonsense is merely scholars dressing up a useful but mundane point with a ton of unnecessary jargon.


My theory is that the jargon creates an artificial barrier to entry. https://t.co/MqLyyppdHl

If one must spend years marinating one's brain in jargon to be perceived as an expert on a topic, it protects the status and earning power of people who study relatively easy topics.

In econ, a similar thing is accomplished by what recent Nobel prize winner Paul Romer calls "mathiness": https://t.co/DBCRRc8Mir

But mathiness and jargon are not quite the same...

Jargon usually doesn't force you to change the substance of your central point.

Mathiness often does. By forcing you to write your model in a way that's mathematically tractable (easy to work with), mathiness often impoverishes your understanding of how the world really works.

has written about this problem:
Time for panel #3: Big Tech and regulation!

I will be live-tweeting again, and you can also watch video at either the Twitter or Facebook links below!


Kaissar: Every industry gets regulated when it gets big. The question is what kind of regulation Big Tech will get,and whether the companies will be proactive in shaping it.

Kaissar: More profitable companies have higher returns. Why? Maybe it's a risk factor, because more profit = higher risk of getting regulated.

Bershidskyis showing a diagram of GDPR complaince pop-ups. What a massive ill-conceived bureaucratic mess.

Ritholtz: It's 2018 and we're still talking about Facebook privacy settings?! If you're still giving your personal data to Facebook, you just don't care about privacy!

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So the cryptocurrency industry has basically two products, one which is relatively benign and doesn't have product market fit, and one which is malignant and does. The industry has a weird superposition of understanding this fact and (strategically?) not understanding it.


The benign product is sovereign programmable money, which is historically a niche interest of folks with a relatively clustered set of beliefs about the state, the literary merit of Snow Crash, and the utility of gold to the modern economy.

This product has narrow appeal and, accordingly, is worth about as much as everything else on a 486 sitting in someone's basement is worth.

The other product is investment scams, which have approximately the best product market fit of anything produced by humans. In no age, in no country, in no city, at no level of sophistication do people consistently say "Actually I would prefer not to get money for nothing."

This product needs the exchanges like they need oxygen, because the value of it is directly tied to having payment rails to move real currency into the ecosystem and some jurisdictional and regulatory legerdemain to stay one step ahead of the banhammer.