Reaction to Katharina Pistor’s book “The Code of Capital”

I recently read this book and decided that I will include it in the syllabus of my Economic Inequality course. A few days ago, when I indicated on Twitter my intention to write about the book in this blog, I was intending a review. However, I found good reviews online, to which my own review would have little to add. These are: a post in the Law and Political Economy blog by Sam Moyn, and this piece by Rex Nutting on MarketWatch. To these, I can add little of value from the point of view of a legal scholar, such as Sam Moyn, or a commentator on political economy, such as Rex Nutting. Instead, I will quote from the publisher’s online blurb, so you can get a quick idea what the book is about, before proceeding with my comments.

Capital is the defining feature of modern economies, yet most people have no idea where it actually comes from. What is it, exactly, that transforms mere wealth into an asset that automatically creates more wealth? The Code of Capital explains how capital is created behind closed doors in the offices of private attorneys, and why this little-known fact is one of the biggest reasons for the widening wealth gap between the holders of capital and everybody else.

In this revealing book, Katharina Pistor argues that the law selectively “codes” certain assets, endowing them with the capacity to protect and produce private wealth. With the right legal coding, any object, claim, or idea can be turned into capital—and lawyers are the keepers of the code. Pistor describes how they pick and choose among different legal systems and legal devices for the ones that best serve their clients’ needs, and how techniques that were first perfected centuries ago to code landholdings as capital are being used today to code stocks, bonds, ideas, and even expectations—assets that exist only in law.

I am intrigued by this book, in my capacity as an economist, for two main reasons.

  1. The book gives a new and insightful perspective on the nature of capital, not long after Thomas Piketty’s Capital in the Twenty-First Century, a book most certainly discussed in my course on economic inequality. One big criticism of Piketty’s concept of capital, leveled by other economists, is that it diverges from the standard use of “capital” in macroeconomic / growth theory, even though Piketty does appeal to some results from this theory in his analysis. Pistor offers in her book an intriguing definition of capital as the aggregation of a myriad strategies of highly-paid lawyers, who shop around existing legal systems to create encodings of assets into concepts that can be defended as being legal in some court of a recognized state, encodings that serve to make up assets out of “thin air” and make these assets long-lived, accumulating over time, and convertible to money when their owners desire. I am not a macroeconomist, but I am eager to see what my colleagues in that field will come up with by engaging with this definition. After all, Paul Romer’s 2018 Nobel prize was for his incorporation of ideas into growth theory, as boosting the productivity of all other inputs to production (yes, I am simplifying). Intellectual protection legal regimes matter for this for obvious reasons. Pistor essentially says that the ideas of lawyers are part of this process. She explicitly discusses how these lawyerly inventions have expanded the scope of intellectual property protection (simultaneously shrinking the public domain in the realm of ideas), but she says so much more about these lawyerly inventions that there ought to be plenty of material here for some new macroeconomic theory.
  2. The second reason this book intrigues me is that it suggests a diagnosis for the disease of ever-increasing inequality in incomes and wealth levels, with the attendant problems of social polarization, undermining of democratic systems and norms, and empowerment of more and more economic and political oligarchy. It is not the job of a law professor like Pistor to suggest to economists interested in political economy and mechanism design how to think about modeling a way forward to formulate effective social and policy responses to these trends. But she has done all such economists (and I do count myself as part of this group) a favor by her diagnosis. I hope the policy designs and suggestions from economists are not long in coming.

How should we teach introductory economics?

I came across this piece by Dylan Matthews in Vox today. It talks at length about Raj Chetty’s new introductory economics course at Harvard, “Economics 1152: Using Big Data to Solve Economic and Social Problems“. Let me see if I have something useful to add to the discussion of how best to teach economics, especially at the introductory level.

I should start with a disclaimer. I came to economics because of my love of mathematics. I wanted to keep doing mathematics for the rest of my life, but to do it in a field of inquiry that might have social value. On the face of it, this would make you, gentle reader, expect that I would have a negative opinion of a course that eschews abstract theorizing and mathematical tools in teaching economics.

On the contrary, I am all in favor of such an approach, when it’s not the only kind of teaching of economics we do. As I tell all my students who ask seriously about the need for all that mathematics, the economy is massively complicated (nobody has disagreed yet with me on this), and therefore we need to employ the most powerful tools that we can in studying it. These tools can come from mathematics, statistics, political philosophy, history, or anthropology — the more toolbox raids we conduct across the academic disciplines, the better off we are as economists.

I happen to have a small comparative advantage in employing and teaching the tools of mathematics, and so this is mainly what I do in the classroom, although I have been restless and have taught all sorts of different topics, not all with an abstract, math-focused approach. Lately, for example, I have been teaching a new course called Economic Inequality, and indeed Raj Chetty’s work, mentioned by Dylan Matthews in the article linked above, has been taken seriously in this course.

I want to consider a point that Matthews’s article mentions, in my own words and with my own emphasis, I want to mention one more thing about the benefit of mathematical work in developing economic theories, and finally another worthwhile effort at revamping the teaching of introductory economics, one that Matthews does not mention, but one that is well aligned with Chetty’s emphasis in his course.

  1. Data never speaks by itself. We need abstract theorizing and we need serious empirical testing of the theories, and we need this to keep going back and forth. As Chetty says, his course is a good complement to the standard Econ 10 at Harvard, not a substitute. My reason for defending building abstract economic theories is fundamental: if we teach students to jump straight to the data and quasi-natural experiments, that’s all to the good except that then they will be less transparent about the theories that inevitably stand behind what they do with the data. I believe this is a serious issue and burdens everyone doing any kind of science, and it really doesn’t matter if you dislike the approach of Russ Roberts, who is quoted by Matthews as saying basically the same thing. As Matthews says, “Chetty isn’t averse to theory. Much of his work is motivated by a desire to poke at and test widespread theories”, so Chetty is perfectly fine with the coexistence of theory and empirical testing. Hurray for that! My reason for belaboring the point here is that I can see the danger lurking in the minds of students who are annoyed by the difficulty and abstraction of economic theorizing to declare all theory BS and to think that they can understand the economy only with data analysis. I would love for such student skeptics to use the econometric techniques they are learning eagerly to bash economic theories and to help build better theories, theories explicitly stated. I do not want anyone to be a “slave to some defunct economist”, as John Maynard Keynes said, without even realizing it.
  2. Relying only on quasi-experiments to discuss economic news and developments in policy-making is all well and good except when we just don’t have plentiful (or any) data generated in such a way for some topics. Do we then ignore topics like this? Could we not be better off applying theories to make some headway, always being careful to qualify our work by emphasizing the limitations of our theories and the need to test them better? Incidentally, stating our economic theories mathematically forces us to be explicit about their limitations by stating our assumptions. This feature alone, in my mind, justifies the (admittedly large) set-up cost of learning enough mathematics to work with abstract economic theories (so it gets the only bold-faced word in this post).
  3. Finally, Matthews could have cited the fantastic work done by in preparing extensive materials for teaching economics, indeed in a fashion congruent with Chetty’s approach. I understand that this was not the point of his article, but I think that the Core Econ project deserves much wider publicity than it is getting. The more I look at it, the more I feel like arguing for a big change in how my own department teaches principles of economics. I certainly plan to use the Core Econ ebook more in my Economic Inequality course come the Spring, when I teach it again.