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

Today's @bopinion post is about how poor countries started catching up to rich ones.

It looks like decolonization just took a few decades to start

Basic econ theory says poor countries should grow faster than rich ones.

But for much of the Industrial Revolution, the opposite happened.
https://t.co/JjjVtWzz5c

Why? Probably because the first countries to discover industrial technologies used them to conquer the others!

But then colonial empires went away. And yet still, for the next 30 years or so, poor countries fell further behind rich ones.
https://t.co/hilDvv0IQV

Why??

Possible reasons:
1. Bad institutions (dictators, communism, autarkic trade regimes)
2. Civil wars
3. Lack of education

But then, starting in the 80s (for China) and the 90s (for India and Indonesia), some of the biggest poor countries got their acts together and started to catch up!


Global inequality began to fall.
1/I'm thinking about the end of Apu in the context of the national debates on immigration and diversity.


2/Apu's presence in Springfield represented a basic reality of America in the late 20th and early 21st century: the presence of nonwhite immigrants.

3/As Tomas Jimenez writes in "The Other Side of Assimilation", for my generation, immigrants from India, China, Mexico, and many other countries aren't strange or foreign. On the contrary, they're a

4/But that America I grew up with is fundamentally ephemeral. The kids of immigrants don't retain their parents' culture. They merge into the local culture (and, as Jimenez documents, the local culture changes to reflect their influence).

5/Simpsons character don't change. But real people, and real communities, do. So a character who once represented the diversity that immigrants brought to American towns now represents a stereotype of Indian-Americans as "permanent foreigners".

More from Society

I've seen many news articles cite that "the UK variant could be the dominant strain by March". This is emphasized by @CDCDirector.

While this will likely to be the case, this should not be an automatic cause for concern. Cases could still remain contained.

Here's how: 🧵

One of @CDCgov's own models has tracked the true decline in cases quite accurately thus far.

Their projection shows that the B.1.1.7 variant will become the dominant variant in March. But interestingly... there's no fourth wave. Cases simply level out:

https://t.co/tDce0MwO61


Just because a variant becomes the dominant strain does not automatically mean we will see a repeat of Fall 2020.

Let's look at UK and South Africa, where cases have been falling for the past month, in unison with the US (albeit with tougher restrictions):


Furthermore, the claim that the "variant is doubling every 10 days" is false. It's the *proportion of the variant* that is doubling every 10 days.

If overall prevalence drops during the studied time period, the true doubling time of the variant is actually much longer 10 days.

Simple example:

Day 0: 10 variant / 100 cases -> 10% variant
Day 10: 15 variant / 75 cases -> 20% variant
Day 20: 20 variant / 50 cases -> 40% variant

1) Proportion of variant doubles every 10 days
2) Doubling time of variant is actually 20 days
3) Total cases still drop by 50%
Two things can be true at once:
1. There is an issue with hostility some academics have faced on some issues
2. Another academic who himself uses threats of legal action to bully colleagues into silence is not a good faith champion of the free speech cause


I have kept quiet about Matthew's recent outpourings on here but as my estwhile co-author has now seen fit to portray me as an enabler of oppression I think I have a right to reply. So I will.

I consider Matthew to be a colleague and a friend, and we had a longstanding agreement not to engage in disputes on twitter. I disagree with much in the article @UOzkirimli wrote on his research in @openDemocracy but I strongly support his right to express such critical views

I therefore find it outrageous that Matthew saw fit to bully @openDemocracy with legal threats, seeking it seems to stifle criticism of his own work. Such behaviour is simply wrong, and completely inconsistent with an academic commitment to free speech.

I am not embroiling myself in the various other cases Matt lists because, unlike him, I think attention to the detail matters and I don't have time to research each of these cases in detail.

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