Posts in category Data Science

May 1, 2018 in Data Science, Economics, Falsifiable

The Scale of Inequality

When dealing with questions of inequality, I often get boggled by the sheer size of the numbers. People aren’t very good at intuitively parsing the difference between a million and a billion. Our brains round both to “very large”. I’m actually in a position where I get reminded of this fairly often, as the difference can become stark when programming. Running a program on a million points of data takes scant seconds. Running the same set of operations on a billion data points can take more than an hour. A million seconds is eleven and a half days. A billion seconds 31 years.

Here I would like to try to give a sense of the relative scale of various concepts in inequality. Just how much wealth do the wealthiest people in the world possess compared to the rest? How much of the world’s middle class is concentrated in just a...

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Apr 19, 2018 in Data Science, Economics, Falsifiable

Is Google Putting Money In Your Pocket?

The Cambridge Analytica scandal has put tech companies front and centre. If the thinkpieces along the lines of “are the big tech companies good or bad for society” were coming out any faster, I might have to doubt even Google’s ability to make sense of them all.

This isn’t another one of those thinkpieces. Instead it’s an attempt at an analysis. I want to understand in monetary terms how much one tech company – Google – puts into or takes out of everyone’s pockets. This analysis is going to act as a template for some of the more detailed analyses of inequality I’d like to do later, so if you have a comment about methodology, I’m eager to hear it.

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Nov 23, 2017 in Data Science, Literature, Model

Two Ideas Worth Sharing From ‘Weapons of Math Destruction’

Recently, I talked about what I didn’t like in Dr. Cathy O’Neil’s book, Weapons of Math Destruction. This time around, I’d like to mention two parts of it I really liked. I wish Dr. O’Neil put more effort into naming the concepts she covered; I don’t have names for them from WMD, but in my head, I’ve been calling them Hidden Value Encodings and Axiomatic Judgements.

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Nov 19, 2017 in Data Science, Literature, Model

Two Fallacies From ‘Weapons of Math Destruction’

I recently read Weapons of Math Destruction by Dr. Cathy O’Neil and found it an enormously frustrating book. It’s not that whole book was rubbish ­– that would have made things easy. No, the real problem with this book is that the crap and the pearls were so closely mixed that I had to stare at every sentence very, very carefully in hopes of figuring out which one each was. There’s some good stuff in here. But much of Dr. O’Neil’s argumentation relies on two new (to me) fallacies. It’s these fallacies (which I’ve dubbed the Ought-Is Fallacy and the Availability Bait-and-Switch)...

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Feb 20, 2017 in Data Science, Politics

Thoughts (and Data) on Charity & Taxes

The other day, I posed a question to my friends on Facebook:

Do you think countries with higher taxes see more charitable donations or fewer charitable donations? What sort of correlation would you expect between the two (weak positive? weak negative? strong positive? strong negative?). I just crunched some numbers and I'll post them later. First I want to give people a chance to guess and test their calibration.

I was doing research for a future blog post on libertarianism and wanted to check one of the fundamental assumptions that many libertarians make: in the absence of a government, private charity would provide many of the same social services that are currently provided by the government.

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