If you ask the average person what ‘science’ is, they’ll probably answer something like ‘it’s what we know about the world’. To the lay person, ‘science’ is a body of facts. To the trained scientist, however, ‘science’ means something different. It’s not a body of knowledge. It’s a method for determining what’s true and what’s not.

Do you think that the discipline of economics is a sham — an ideology masquerading as science? If so, here is a reading list for you. These 10 books have influenced my thinking over the years. Read them and join me in the journey of the economic heretic. 1. Capital as Power. A Study of Order and Creorder

Over the past few years, I’ve spent a huge chunk of time learning how to make beautiful graphics using R ggplot. In this post, I’m going to share what I’ve learned.

In Problems With Measuring Inequality, I discussed how inequality is an ambiguous concept. The problem, in short, is that a single metric can never capture every aspect of a distribution of income. Much like we cannot tell the shape of an object from its perimeter or area, we cannot tell the shape of the distribution of income from a single metric of inequality. From this fact arise some seeming paradoxes.

Did you hear the joke about the economists who tested their theory by defining it to be true? Oh, I forgot. It’s not a joke. It’s standard practice among mainstream economists. They propose that productivity explains income. And then they ‘test’ this idea by defining productivity in terms of income . In this post, I’m going to show you this circular logic.

For the last few weeks, I’ve been thinking about causation in the social sciences. As with many instances of reflection, this was prompted by rejection. A political economy journal recently rejected a paper that I had submitted. The paper (available here) studied the correlation between hierarchical power and income.

Economists often talk about income inequality the same way a doctor would talk about a child’s height. Just as a doctor would say “Sylvia continues to grow taller”, economists say things like “US income inequality continues to grow”. (Full disclosure, I’m sure I’ve said similar things). On the surface, there seems to be nothing wrong with this type of pronouncement.

Over the past few weeks, I’ve been thinking a lot about Aaron Swartz. Swartz was an internet pioneer who, in his teens and early 20s, made huge contributions to computer culture.

In this post, I’m going to celebrate an anniversary of sorts. It’s been 4 years since I stumbled on a striking relation between inequality and capitalist income. In the United States, the income share of the top 1% is tightly related to the share of corporate dividends in national income. Over the last four years, I’ve developed a model that explains this relation.

Scientists live and die by their scientific ‘impact’. For the uninitiated, ‘impact’ is a measure of a scientist’s contribution to their field. While there are many measures of scientific impact, almost all of them focus (in some way) on citations . So if more people cite your papers, you have more scientific ‘impact’. The idea behind counting citations is that they quantify other people’s engagement with your ideas.

In a recent blog post called “How Not to Measure Inequality”, the anthropologist Jason Hickel argues that economists measure inequality the wrong way. Hickel thinks that standard measures of inequality (such as the Gini index), underestimate global disparities. The problem, according to Hickel, is that economists measure relative inequality. Hickel thinks we should measure absolute inequality.