I have previously written about how to evaluate and think about public debt in stable, developed countries. There, the overall message was that the dangers of debt were often (but not always) overhyped and cynically used by certain politicians. In a throwaway remark, I suggested the case was rather different for developing countries. This post unpacks that remark. It looks at why things go so poorly when developing countries take on debt and lays out a set of policies that I think could help developing countries that have high debt loads.

The very first difference in debt between developed and developing countries lies in the available terms of credit; developing countries get much worse terms. This makes sense, as they’re often much more likely to default on their debt. Interest scales with risk and it just is riskier to lend money to Zimbabwe than to Canada.

But interest payments...

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[Epistemic Status: I am not an economist. I am fairly confident in my qualitative assessment, but there could be things I've overlooked.]

Vox has an interesting article on Elizabeth Warren’s newest economic reform proposal. Briefly, she wants to force corporations with more than $1 billion in revenue to apply for a charter of corporate citizenship.

This charter would make three far-reaching changes to how large companies do business. First, it would require businesses to consider customers, employees, and the community – instead of only its shareholders – when making decisions. Second, it would require that 40% of the seats on the board go to workers. Third, it would require 75% of shareholders and board members to authorize any corporate political activity.

(There’s also some minor corporate governance stuff around limiting the ability of CEOs to sell their stock which I think is an idea...

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[Epistemic Status: Open to being convinced otherwise, but fairly confident. 11 minute read.]

As interest in how artificial intelligence will change society increases, I’ve found it revealing to note what narratives people have about the future.

Some, like the folks at MIRI and OpenAI, are deeply worried that unsafe artificial general intelligences – an artificial intelligence that can accomplish anything a person can – represent an existential threat to humankind. Others scoff at this, insisting that these are just the fever dreams of tech bros. The same news organizations that bash any talk of unsafe AI tend to believe that the real danger lies in robots taking our jobs.

Let’s express these two beliefs as separate propositions:

1. It is very unlikely that AI and AGI will pose an existential risk to human society.
2. It is very likely that AI and AGI will result in widespread unemployment.

Can you spot the contradiction between these two statements? In the common imagination, it would require an AI that can approximate human capabilities to drive significant unemployment. Given that humans are the largest existential risk to other humans (think thermonuclear war and climate change), how could equally intelligent and capable beings, bound to subservience, not present a threat?

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There are many problems that face modern, developed economies. Unfortunately, no one agrees with what to do in response to them. Even economists are split, with libertarians championing deregulation, while liberals call for increased government spending to reduce inequality.

Or at least, that’s the conventional wisdom. The Captured Economy, by Dr. Brink Lindsey (libertarian) and Dr. Steven M. Teles (liberal) doesn’t have much time for conventional wisdom.

It’s a book about the perils of regulation, sure. But it’s a book that criticizes regulation that redistributes money upwards. This isn’t the sort of regulation that big pharma or big finance wants to cut. It’s the regulation they pay politicians to enact.

And if you believe Lindsey and Teles, upwardly redistributing regulation is strangling our economy and feeding inequality.

They’re talking, of course, about rent-seeking.

Now, if you don’t read economic literature, you probably have an idea of what “rent-seeking” might...

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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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