Follow up on my tweets from yesterday complaining about the new economist report on how “autocratic” countries over-state their GDP. I will analyze the paper this article references, and show a range of sleight-of-hand and maths errors in this work.

https://twitter.com/drStuartGilmour/status/1580855712313667585?s=20&t=ORiR32ut-WXY1V_jQEW4DA

This is an excellent and egregious example of how economists don’t understand and/or misuse statistical tools. I will be referencing this version of the work the Economist is discussing – there are many versions, this one is the most recent (2021). bfi.uchicago.edu/wp-content/upl…

It’s so exhausting dealing with this torrent of bad-faith data “analysis” from the Economist. The latest is an analysis of satellite data on night time lights and gdp growth that suggests “autocratic” states fiddle their numbers on gdp growth. economist.com/graphic-detail… For starters it’s obvious bad faith. This figure from the report it references shows the gdp growth and night time light growth for “free” and not-“free” countries. The assumption this is dishonest rather than just a different growth relationship is so condescending.

@dakekang and @huizhong_wu your reporting on prison rates in your latest article about Xinjiang is wrong and misleading. Assuming your linked list is true, the imprisonment rate is not “the highest anywhere in the world” and your numbers are just wrong.apnews.com/article/religi… First, you report that US prison rates are 364 per 100000. This is not correct. The number is actually 537, but you didn’t include US Jails in your figure. Please correct it. We don’t need more articles understating the USA’s incarceration epidemic.

Hong Kong has experienced a wave of #covid19 cases and deaths, and some media are blaming this on Chinese COVID vaccines, saying they don’t work. Let’s talk about whether this is true, and the implications for global vaccine equity of vaccine misinformation. A recent presentation by Hong Kong University (HKU) professors has been used by the usual China “experts” and journalists to argue that reliance on China’s vaccine, Sinovac, compared to BioNTech’s mRNA vaccine was a bad idea, with tweets like this.

Remember in 2020 there was a map showing how well-prepared different countries were, which received widespread derision for its terrible accuracy? I analyzed the underlying data to see how poorly it predicted pandemic outcomes. The map is based on the Global Health Security Index, a numerical measure of pandemic preparedness compiled by Economist Impact in collaboration with Johns Hopkins and others. There is a published report, with a clear methodology.
ghsindex.org

This week 10 years ago I first visited Minamisoma, and began a five year long collaboration with the local community studying nuclear, tsunami and earrthquake disaster response and recovery. A thread about my research and what we learned. When the Great East Japan Earthquake and Tsunami hit I was a brand new assistant prof, living in Tokyo for two weeks. Almost as soon as it happened all the Japanese students from the dept I worked in headed north to help with recovery. [I took these photos in Feb 2012]

Cases and hospitalizations are rising in the UK. On NYE I built a prediction model of hospitalizations, and warned even if omicron is lower risk things were going to be bad. Let’s check in on my predictions now data is being released after the holidays.

https://twitter.com/drStuartGilmour/status/1476807392407228416?s=20

First, I predicted hospitalizations to the end of last year based on new cases. Here is how my prediction (red line) compares to past observations (the solid dots) and the new hospitalization data released this year, up to 28th December (hollow circles). Far above my prediction!

Will the UK soon see a tidal wave of COVID-related hospital admissions? Is Omicron really causing a drop in hospitalizations? Let’s look at the data. I’m spending the afternoon watching the year end mixed martial arts, #rizin33, so it’s as good a time as any to examine the new surge in cases in the UK. There’s a lot of talk that omicron is less severe and hospitalizations won’t rise, but I’m not convinced. Let’s look.

The #economist is publishing a regularly-updated model of excess deaths due to COVID, which is being used to tell just-so stories about countries the Economist doesn’t like. Let’s talk about the dangers of using machine learning for analysis when you don’t have data. Before we do, let’s just take a moment to appreciate the deep racism of this picture from a report using the Economist's numbers by the Centre for Strategic and International Studies. When you see a cover pic like this, you can guess what the contents are gonna be.

In September 2021 an anti-vaccination meme started which claimed 70 members of Pfizer’s “investment board” are members of China’s Communist Party. Let’s look at how major news services and anti-China thinktanks drive anti-vaccination sentiments that kill Americans. Snopes targeted this meme as false in September 2021, and briefly mentioned a news article in December 2020 that released the names, and says no government has verified it. Its role in helping to fuel anti-vax sentiment is clear from reddit subs like r/HermanCainAward.

This table shows the rate of imprisonment of minorities in a couple of different countries. Let’s talk about genocide denialism among China “experts” who are laser-focused on proving genocide in Xinjiang while they ignore the legacy of genocide in their own countries. These China “experts” are intent on uncovering these statistics and on activism in international forums to challenge mass imprisonment in China. Meanwhile their own countries are imprisoning indigenous people or minorities in huge numbers but they say nothing. Why?

The Blitz (nazi terror bombing campaign of the UK) killed 40-50,000 people in a population of 41 million. #COVID19 has killed 130,000 people in a population of 65 million people – it is nearly twice as bad as the blitz. Let’s compare responses. In response to the bombing of urban centres the UK Tory government evacuated a million women and children to the countryside, introduced a curfew, enforced a blackout and banned certain forms of speech harming the war effort.

For those wondering at why the Australian government would abandon its Afghan “allies” and comrades of ADF soldiers to an uncertain fate after the Taliban have taken over #Afghanistan, a little history lesson in how Australia has historically treated Afghan refugees In August 2001 the MV Tampa rescued 433 refugees at sea. 244 of them were Afghans, fleeing the Taliban, and following the laws of the sea Tampa attempted to land them in Australia. The Australian government refused to take them.

This figure shows the number of births that were “lost” in Japan due to falling birthrates since 2010. Nearly 1 million over just 10 years! Shocking! But no outcry from western thinktanks. Let’s discuss attributing sinister motives to good policy in @adrianzenz 's latest work 2/ here is the report, it’s a preprint and also accepted at the journal Central Asian Survey. papers.ssrn.com/sol3/papers.cf…

@adrianzenz @Nrg8000 @ASPI_org @GuardianAus This figure shows the change in birth rates in Japan from 1985 to 1990, with the municipalities where the change was >30% shown in red. Let’s talk about the problem with using demographic data to try and prove genocide. @adrianzenz @Nrg8000 @ASPI_org @GuardianAus As you can see from that figure, birth rates in some municipalities (市区町村) in Japan dropped by more than 50%. Here are some example trajectories from 1970 – 2010.

Since I have a lot of new followers, many from China, I thought I would share some of my research with Chinese colleagues that I have been working with since 2011. In particular I work a lot with colleagues in Sun Yat Sen U, on health system and HIV topics in China. [1/9] First, I have worked with colleagues at SYSU on mathematical models of HIV in men who have sex with men (MSM) in China, to estimate the benefits of Pre-exposure prophylaxis (PrEP) [2/9] bmcinfectdis.biomedcentral.com/articles/10.11…

@aspi_icpc @Nrg8000 @jleibold
I reanalyzed the data from your “family de-planning” report. Contra your findings, “coercive birth control policies” in Uyghur-majority areas had less effect than in Uyghur-minority areas. How do you explain this? @aspi_icpc @Nrg8000 @jleibold In fact we can build a better model that shows “coercive birth-control policies” were less effective in Uyghur areas. It explains more of the variation than the figures in your report. Why did you not present this model?