Three Cheers for the Empirical Muckraker

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. It requires pulling together disparate sources — using datasets that were never designed to go together. It requires endless hours of searching the internet. And before the internet, it required actually going to a library. You had to move your feet!

In data science, computational power is everything. In empirical muckraking, the researcher’s resolve is what matters. Empirical muckraking is painstaking and unsexy. But it’s absolutely necessary for good social science.

Why? In a word, history. Data science is blind to history. You know those reams of data generated by social media? They go back about 10 years. Although we may forget, the world did exist before Facebook, Twitter, and Instagram. And unlivable as it was, that dark age needs to be studied.

Studying economic history requires empirical muckraking. The farther back you want to go, the more tedious it gets. But it can lead to spectacular new knowledge. So let’s celebrate some great examples of empirical muckraking.

Nitzan and Bichler’s Buy-to-Build Indicator

During the 1990s, corporate mergers became part of the public zeitgeist. But what is the deep history of mergers and acquisitions?

Jonathan Nitzan and Shimshon Bichler piece together the puzzle with their ‘buy-to-build’ indicator. This indicator measures the dollar value of mergers and acquisitions expressed as a percentage of gross fixed investments. It tells us how much corporations are spending on buying other companies, relative to how much they are spending on actually building things. Nitzan and Bichler muckrake to put together a century of US data:

buy_to_build
Nitzan and Bichler’s Buy-to-Build Indicator (Source)

More recently, Joe Francis compiled an open source update of the buy-to-build indicator. This is great empirical muckraking.

Angus Maddison’s Deep Economic History

Angus Maddison was a British economic historian. Over his lifetime he muckraked to create a dataset of long-run economic growth. For most economists, ‘long-run’ means 100 years. But Maddison went back 2000 years. It’s a monumental accomplishment.

Now, I am very critical of real GDP as a measure of economic scale. I’ve written a paper about the problems with real GDP, and I hope to have a blog post about it shortly (I’m going to collaborate with Nitzan and Bichler on this). But my skepticism of real GDP does not diminish the importance of Maddison’s work. Maddison died in 2010. Thankfully the Maddison Project Database continues to host this important data.

Vaclav Smil — The Energy Oracle

Vaclav Smil is by far the most prolific energy muckraker out there. I’m not going to try to summarize his research. Just look at his list of publications here.

But I will leave you with a beautiful chart of Smil’s data. Gail Tverberg has used Smil’s data to make a fantastic series of charts on world energy consumption. She uses Angus Maddison’s population data too. See the whole series here. Below is Tverberg’s chart for energy use per capita. Credit to Tverberg for such a pretty chart, and credit to Smil for muckraking the energy data.

per-capita-world-energy-by-source
Gail Tverberg’s chart of world energy use per capita. She uses data from Vaclav Smil. (Source)

Thomas Piketty — Modern Inequality

I don’t think I need to say much about Thomas Piketty. He’s probably the most famous economist in the world. Since publishing Capital in the Twenty-First Century, he’s become an academic rock star.

Many heterodox economists think Piketty’s theories of inequality are naive. I would be one of those economists. But this does not detract from Piketty’s empirical work. He’s a prolific inequality muckraker.

Together with other researchers, Piketty has created the World Inequality Database. It’s an invaluable tool for understanding modern inequality. I’ll leave you with one of Piketty’s most famous charts. Here’s the history of inequality in the United States:

Graph-1
This may be the most famous chart in modern economics. It’s Piketty’s data for the income share of the top 10% in the United States. (Source)

Branko Milanović — Ancient Inequality

Branko Milanović is the oracle of ancient inequality. He has muckraked to compile inequality data for many pre-industrial societies. Here’s a chart of inequality versus GDP per capita for ancient societies:

milanovic
Ancient Inequality Data from Branko Milanović. (Source)

The curved line is the ‘inequality possibility frontier’. This is the maximum inequality that is achievable at the given level of production. I have my own ideas about what creates an inequality frontier — I think it has to do with the growth of hierarchy. I’ll write about these ideas later. But I’m glad that Milanović has done the tedious muckraking so that I can come along and do ‘high theory’. Kudos to Milanović.

… And many others

This has been a brief tour of the empirical muckrakers who I admire. There are countless others doing important work. So don’t send me an angry email if I didn’t put you on the list.

Let’s hope that as big data envelops academic research, the noble tradition of empirical muckraking continues.


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