Yesterday I was reminded of what got me interested in economics. I’ll preface this by saying that I make my living as a substitute teacher in Toronto. It’s not glamorous, but it pays the bills. It gives me time to do research from outside academia. When I’m in high school classrooms, I always browse the posters on the wall. It’s funny what you see. You find things (both good and bad) that you’d never see in institutions of ‘higher learning’.

Do you believe in free markets? Do you think that unfettered competition is the best way to organize society? If so, this post is intended to shake your faith. No, I’m not going to argue that free markets are bad. Instead, I’m going to show you some evidence that may disturb you. I’m going to show you that as societies develop, they kill off the free market and replace it with a command economy. Let me be clear about what I mean.

In this post, I’m going to return to the relation between energy and institution size. When we left off last time (in Groping in the Dark), I had described my struggle to understand how the size of firms and governments changes with energy use. It took me years of empirical muckraking to discover four stylized facts.

In this post, we’re going to go down the rabbit hole looking at the severe problems with real GDP. I am honored to have coauthored this research note with Jonathan Nitzan and Shimshon Bichler. Jonathan teaches political economy at York University in Canada. Shimshon teaches political economy at colleges and universities in Israel. All of Bichler &

In this post we’re going to take a journey into the world of power-law distributions. Power laws pop up again and again in my research. But I’ve never taken the time to discuss what makes them so weird. This post will be a little ‘power-law primer’ that I’ll reference in future blog posts. Let’s start by defining the word distribution . A ‘distribution’ refers to how data is spread out. I’ll use human height as an example.

In the last post, I celebrated the noble pursuit of empirical muckraking. Now I have a muckraking challenge for the empirical researchers of the world. The Deep History of Energy and Institution Size Over the last 5 years, I have pieced together an interesting relation between energy and institution size.

Data science is all the rage right now. The data scientist uses complicated algorithms and sophisticated statistics to analyze reams of ‘big data’. Don’t get me wrong — I’m all for analyzing big data. But I don’t want to forget about an equally important form of empirical research: muckraking . Empirical muckraking is the slow, tedious process of searching for scattered data.

In science, being wrong is call for a celebration. Now that you think I’m crazy, let me explain. Science is about the search for truth . Logically, this means we should celebrate when we are right . But here’s the catch. How do we know when we are right? We can’t. Science is about conditional truth. We have ideas that we think are right because they have not yet been proven wrong.

In the opening post of this blog, I described my ‘top-down’ approach to studying society. This means studying groups of people without trying to reduce everything to the actions of individuals. It’s not that I think individual actions are unimportant . Of course they are important. The problem is that we are hopelessly far from understanding the complexities of the human psyche. In this post, I’m going to dive into the problem.

In this post we’re going to take a break from my research. Instead, I’m going to subject you to my crackpot theories of psychology. This post is called “Why Scientist Can’t Run Daycares”. But I’m just assuming that other scientists are like me. So maybe a better title would be “why I can’t run a daycare”. What prompted this post? Well, you should know that I have a 3 year old daughter. She’s the joy of my life, a true delight to be around.

There is an exciting side of blogging that I want to explore here. Blogging can tell the story behind research. This is something you don’t get in journals. Most scientific articles obey a formula that goes like this: Here is the question I asked. Here is how I answered the question. Here is what I found. This formula makes the article easy to read, since we know what to expect.