I had a statistical conniption about the Aberdeen study published in BMJOpen earlier today. Let's see if I can explain my concerns in a less techie fashion. The gist of the article is that countries with more flights arriving had more deaths. That so? 1/n

bbc.co.uk/news/uk-scotla…
The article, which you can read at bmjopen.bmj.com/content/bmjope… relies on the following scatterplot to make its point. Indeed, there does appear to be a relationship between arrivals and (logged) daily death rates. 2/n
That graphical presentation is supported by a multivariate statistical model, which shows that the result is robust to including lots of other confounds - (log) population, income, age profile, health status, density, etc. So far so good. 3/n
But... eagle-eyed viewers might have looked at that graph and thought - wait a second, countries with lots of international arrivals probably have large populations and they might also have lots of people dying from COVID every day. You know, because they're large. 4/n
Now the statistical model has a control for population size - that means it's looking at the relationship between the number of arrivals and the number of deaths, netting out the 'effect' of population. That feels ok... but... 5/n
Wouldn't it just make more sense to normalize flights and deaths per head of population? That way we really have measures of risk of death and how internationally connected a given country is. And once we do that... 6/n
Psyche! The relationship is still there. In a bivariate sense. Good news. Except, now when we try and control for other things, such as income or region, the whole thing blows up. Let's see that 7/n
Let's begin with income. If you look at the graph I just showed you'll notice the kinds of countries that are towards the top-right of the graph are wealthy and those towards the bottom left are poorer. So let's see what happens if we split the sample into four income groups. 8/n
Now we see that poorer countries (top left) have low numbers of flights and low numbers of deaths. Among richer countries, flights and deaths are generally higher. But moreover, there's no real relationship between flights and deaths except in Box 3. 9/n
I lied earlier when I said there wouldn't be any maths. Let me just show you the difference between a simple linear regression with just flights per capita (left) and another that includes GDP per capita as well (right). You see the *** thingies next to flights? They vanish. 10/n
The same is true if we try and take global region into account. We add an indicator for Europe, Asia, Africa, N America, and S America. We can do a graph... 11/n
Or we can run one of those linear regression thingies with an indicator for each region. Either way, the flights variable basically loses all its power to explain deaths. 12/n
We can see this by looking closely at Europe. There's really very little going on. 13/n
I went into much more (well somewhat more) technical detail earlier (see this thread, which will also allow you to read my code). But I think, given how much press this study got, it's important to try and show things simply. 14/n

The moral of the tale is that the press should be very careful when reading statistical papers with vaguely plausible results but very little data. We really can't say anything for sure from this study and I certainly wouldn't use it to advocate closed borders. 15/n

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More from @benwansell

4 Feb
Since the authors of this study kindly make their data available, we can see that their multivariate finding largely depends on using absolute deaths and flights not per capita. To wit, on the left their results replicated, on the right the per capita version 1/n
Now if we then log flights per capita we can recreate their 'finding' - see below. But I'm somewhat nervous about what that means about the role of outliers. Basically we have too few observations and too much instability of results for me to be comfortable with this. 2/n
The moral of this tale is single-shot cross-country regressions as policy guidance ain't the way to go. This is where we really need something with a stronger claim to causality. You can play with data yourself and see how I created these at github.com/benwansell/COV… 3/n
Read 5 tweets
9 Dec 20
🇬🇧🇪🇺 Who caused Hard Brexit? Some thoughts from the perspective of a social scientist. In the last few days we have seen an interminable debate on whether Remainers, Soft Brexiters, or Hard Brexiters are responsible for Hard Brexit. And it’s a false debate. Why? 1/n
People are confusing 'causes of effects' with 'effects of causes'. What this means is that we are all interested in - the former - why Hard Brexit happened - but using arguments about how one actor did something - the latter - as our explanation. These are different! 2/n
It is completely possible that Remainers not accepting a customs union increased the probability of Hard Brexit - that's an effect of a cause. But that does not imply responsibility. Because there are lots of other causes producing the same effect. 3/n
Read 13 tweets
7 Sep 20
🧑‍🎓MERITOCRACY IN THE NEWS👩‍🎓 @David_Goodhart and Michael Sandel have both written provocative new books about the trouble with 'meritocracy'. Both argue that non-graduates have been undervalued and that graduates in non-graduate jobs are disillusioned. What do the data show? 🧵1/n
The former question is a tough one since there are two issues at stake. 1. Are non-grads elected as politicians? And 2. Are their policy preferences represented? But consensus in polisci is the answers are (a) Not as much as they used to be and (b) Not as much as for the rich.2/n
In terms of elected representation I strongly recommend reading @DrTomD_OG's CPS article on the decline of working class members in the UK parliament (journals.sagepub.com/doi/full/10.11…) or various pieces by @NoamLupu and @Nick_Carnes_ 3/n
Read 22 tweets
18 Apr 20
😷Social Distancing in the UK Update😷 Now with ANIMATIONS🎥Last week (see below) I looked at Google Community Reports on changes in workplace (& other) activity across British regions. I now have an extra week of data & income measures at the regional level. What do we see? 1/n
First off, the Brexit relationship was still there on April 9th (see below) - workplace activity has declined more in 'Remain' areas. The big question is why and I very deliberately was careful about that. Maybe it's because these areas are richer and people work from home? 2/n
We can have a look at that pattern in the figure below, which plots GDP per capita (i.e. personal income) at the local level versus changes in workplace activity from the norm for April 9th. Definitely some (negative) relationship but looks weaker than the 'political' one. 3/n
Read 25 tweets
14 Apr 20
🚨 Where are people social distancing in the UK?🚨 Thanks to the Google Community Reports we can see how people have behaved since March. Good news - social distancing is happening everywhere. Less good news - there is still a divide. And guess what explains it... BREXIT...😬 1/n
The brilliant people at the ONS Data Science Campus have scraped the Google Community Reports and matched them to demographic and regional data. All I have done is further match this to the 2016 referendum vote at the local authority or county level. github.com/datasciencecam… 2/n
Now slightly annoyingly, Google collect activity data at differing levels - sometimes local authority, sometimes county, and just Greater London... But still we can see how demographics and (maybe) politics shape behaviour. And we'll see the culture war wins out... 3/n
Read 26 tweets
17 Feb 20
🚨The problems with democracy coding and bias 🚨 Political scientists among you will know about the Polity IV score. This has been until recently the preferred measure of democracy for many scholars. So why, you may ask, does it not like democracy in US or UK? 1/n
Until quite recently - I'll let you guess the year - Britain and the US were coded as +10 on the Polity score. That's the max in a -10 to +10 scale. Same as Sweden, Germany, Spain, Portugal, Poland, Hungary... 2/n
Polity is made up of 3 components: constraints on the executive, recruitment of the executive, and political competition. The former is checks & balances, middle is how leaders are chosen, and latter is about elections and party competition. Final index comprised of all 3. 3/n
Read 16 tweets

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