I can program and like games

## Category: Politics and Economics

### Understanding why the CDC Gives the Guidelines that it Does

The CDC continues to issue very puzzling guidelines about the coronavirus. The most recent strange recommendation is that even if you already had covid, you should still get the vaccine. Which… makes no sense?

Let’s assume our goal is to reduce the number of deaths. Currently in the US, something like 26 million people have tested positive for SARS-CoV2, and roughly 440,000 people have died of COVID19. I’m using these terms to make a distinction: Not everyone who gets the virus will get sick, and not everyone who gets sick will die. Let’s be generous and assume that 100 million people got the virus so far, then mortality rate is 0.44%.

What do those same numbers look like for people who already had the virus before? Obviously the first number, the chance of getting the virus, should be roughly the same. But your body has fought this virus (or a very similar virus) before, so your chance of getting sick is much lower. For a long time it was uncertain whether you can get sick a second time at all, but now there are enough confirmed cases. How many of those have died? At least one, who was on chemotherapy at the time. Let’s estimate the number at 10, which would get us to a mortality rate of 10/26,000,000 = 0.000038%.

Should we really be giving people with that second risk the vaccine when not everyone in the first group has had a vaccine yet? Either the CDC worked with different numbers that are ten thousand times bigger, or their goal is not to reduce the number of deaths. What could that other goal be?

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### Looking for Voter Fraud (in old elections) with Data Visualization

The 2020 US election is finally over, and all the election excitement of the last week reminded me of something I had been meaning to look into: Sergey Shpilkin’s data visualizations that clearly show fraud in Russian elections.

I generated the same visualizations for all US presidential elections from 2000 to 2016. The result is that I can’t find any evidence of fraud in any of those elections. But the visualizations show clear evidence of voter suppression of democrat voters. On closer inspection that turns out to be the effects of the electoral college system, which leads to a very interesting conclusion: You might already know that if the president was elected by popular vote, the US would only have had four years of republican presidencies from 1992 to 2024, with the rest being democrat. But these visualizations suggest that just looking at the popular vote actually underestimates the distortion of the electoral college. It also acts akin to voter suppression of democrats, without which national politics would swing even stronger to the left than the popular vote suggests. But lets start by looking at Sergey Shpilkin’s work:

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### Concepts for the Current US Mess

An unforeseen disaster is never the consequence of a single factor, but rather is like a whirlwind, a point of cyclonic depression in the consciousness of the world, towards which a whole multiplicity of converging causalities have conspired

– Carlo Emilio Gadda (in That Awful Mess on the Via Merulana)

It’s hard for me to write a focused blog post at the moment because there just seem to be too many active problems. I could have written a focused blog post about a programming topic, but that feels tone-deaf. So instead this will be a scatter-shot blog post about ways of thinking that could help us out of this mess. Also, since I usually write about programming, I will try to feed the lessons back to programming.

For context (if you’re reading this in the future or from another country) the US has had a really bad year. We nearly started a war with Iran, we impeached our president but couldn’t get him out of office, and then we completely failed our response to the global pandemic. After initially doing nothing and hoping it would just go away, the US decided to react in the most costly way possible, causing mass unemployment while still proving mostly impotent in fighting the virus. Now, after that huge sunk cost, we have mostly given up on fighting the coronavirus, just in time for a new problem to arise: Massive amounts of protests all over the country, some of which even turned into riots. The immediate cause is that the police killed another unarmed black man because he was briefly resisting them. But of course it’s pent-up anger from years of police brutality. And of course it couldn’t have come at a worse time with mass-unemployment and a pandemic still raging through the country.

All of this didn’t have to be, so here are some helpful tools of thought:

### The Covid-Shutdowns are Actually a Great Civics Lesson

Currently much of the country is shut down to stop the spread of the coronavirus and there is very active debate about how soon we should open up again. Some people say as soon as possible, others are saying immediately. Those might sound like similar viewpoints, but “as soon as possible” might be anything from two weeks to two months, depending on who you ask. There’s also a lot of debate about how deadly a second wave would actually be if we opened up the country with few or no restrictions. What percentage would get the virus? How many of those would die?

Uncertainty about all of those numbers is slowly decreasing and it seems like the reopening will happen sooner rather than later.

But I want to frame the debate about how it’s actually a great civics lesson. It shows how the government is really of the people, by the people and for the people, and how it can only do things that the people allow it to do. It also neatly shows how we need the government to do things that everyone wants to happen, but that they can’t make happen on their own.

### A New York History of Covid-19, Written at the Half-Way Point

You have to write these things down while you still remember them. I was already beginning to forget that there was a toilet paper shortage. Similarly right now the popular thing is to point out that this was predictable and we should have listened to the experts. But the experts were predicting that this would be much less bad:

The consensus forecast generated by the individual responses indicates that we should expect roughly 19,000 reported cases by March 29

To be fair, they thought that the curve would behave similar as in other countries. They didn’t expect the US government to mess up its response this badly.

Another thing that people are already forgetting is what “flatten the curve” meant. It was supposed to be a strategy to avoid the quarantine lockdown that we all now live in. Western countries didn’t want to do what China did, and “flattening the curve” was the appealing alternative. A lot of these things can only be understood in context, because things are changing so incredibly quickly that it feels like we’re living in a whole new world every couple weeks, and we forget. So lets start at the beginning while we still remember:

### A Summary of the Important Points in Capital in the Twenty-First Century

Capital in the Twenty First Century by Thomas Piketty was widely recognized as a very important book when it came out in 2013. Yet somehow now, in 2018, I rarely encounter people who have learned the lessons from the book. Of course I don’t expect most people to read the book, but since the lessons are so important for development of society, I would expect them to be spread by other means. In order to help that, I decided to write this blog post which summarizes the most important points. So here is the first point:

## 1. The more money you have, the more money you make

This seems to be a fundamental law of economics. It’s not something we have constructed. It’s even true in primitive societies: If there are two families, one family owns two cows, and one family owns ten cows, the family with ten cows doesn’t make five times as much money as the family with two cows, it makes more than that. That’s because it can more easily survive bad times (like if a cow gets sick) or it can invest in better tools to take care of cows, and those tools pay off more (like fences or a cow shed).

### Where do top scientists come from? And what do taxes have to do with it?

I was reading this article recently, which talks about “Where star scientists choose to locate: the impact of US state taxes” It’s a summary of a paper about “the effect of state taxes on the geographical location of top earners.”

It’s a very interesting idea: The problem is that states often lower taxes with the hope of attracting business or talent, but there is very little evidence about whether that actually works. So the authors of that paper decided to find a group of influential people who are somewhat easy to track: people who apply for lots of patents, the so called “star scientists” from the title. So the authors built a huge database, tracking where the top 5% of scientists who applied for the most patents had moved to over the years.

And the authors claim that they found pretty clear evidence that people like to move from high-tax states to low-tax states, so the conclusion is that if you want to attract top scientists, you should lower taxes.

Except, I dug through the data and I found the opposite. Yes, top scientists do move to states that have lower taxes, but high tax states have such a large lead in the number of scientists, that that little bit of migration doesn’t matter. But we’ll have to get to that conclusion one step at a time.