A few hours after posting my long post about economic fables yesterday, I saw a tweet by Noah Smith that linked to this long, worthwhile, lecture by DeLong on what it is to "think like an economist". I may post a reaction to it, but first I need to really absorb its message. On a first skimming, it seems a very good one.
You know to expect a subjective answer, right? OK, then. Here I go.
I am revamping the introductory graduate microeconomics course for the fall and have added to my recommended summer readings that I emailed my students the book Economic Fables by Ariel Rubinstein. This went to their inboxes a few weeks after other recommended readings that are much more technical and was intended to reinforce what I feel is my most ignored injunction to graduate students, which is to be acutely aware of the limits of economic theories they learn and use. I elaborate here on how I hope this reinforcement can happen.
Rubinstein takes an apparently extreme stance in Economic Fables. He denies that economic theory is useful. He calls economic models “fables”. In the interview that you can find in the book’s link given above, he says that a fable is a fantasy story that, if it is a good one, gives us some insight about the real world. But you cannot use a fable to predict real-world outcomes.
For an economic theorist, like Rubinstein and myself, building fantastical worlds in economic models (or fables) is valuable for the insight it offers on the structure of economic activity, and that is value enough for Rubinstein. Creating and studying economic models needs no further argument to justify its presence in Universities, or the salaries of economic theorists. Further, there is a risk that unscrupulous people may point to economic models, written in a mathematical language that is inscrutable to most, to give the discipline of economics an unwarranted aura of being a true science.
After I sent my email to the incoming graduate students, I worried that they would come to our first class in the end of August a little puzzled by this book recommendation. Why did I ask them to read Economic Fables? Doesn’t it undermine the desire to learn microeconomic theory, the subject of our class? Does it not take time away from studying the other materials I asked them to study over the summer to get readier for the class?
Before I offer my answers to these questions, let me link to a recent piece I enjoyed about the good (useful!) recent developments in economics, by Noah Smith. He points out that criticisms of economics that say it has become a religion, with its dogmatic theories, abound. Doesn’t this fit well with Rubinstein’s use of “fables” for “models”?
It fits well, but it is not an accurate criticism, and it is not productive. Lest it seem that I am lumping Rubinstein in the previous sentence, I hasten to add that I read his book as a deliberate provocation to economists and as a potentially very useful one. It is true that economic models by themselves cannot help us predict anything. They are fantasies. How then did economists find so many applications of some of their models that have clearly worked in the real world? Here is a list of such successes, from Noah Smith’s piece:
First, economists have developed some theories that really work. A good scientific theory makes testable predictions that apply to situations other than those that motivated the creation of the theory. Slowly, econ is building up a repertoire of these gems. One of them is auction theory, which predicts how buyers will bid for things like online ads or spectrum rights — Google’s profits are powered by econ theory as much as by search algorithms. Another example is matching theory, which has made it a lot easier to get an organ transplant. A third is random-utility discrete choice theory, which is used in everything from marketing to transportation planning to disaster preparedness.
Nor are econ’s successful theories limited to microeconomics. Gravity models of trade, though fairly simple in nature, have proven very successful at predicting the flow of international trade.
This list includes, of course, the one success that lets economists claim an economic theory has saved lives directly (via organ transplant chains, made possible by the work of economics Nobel prize winner Alvin Roth and colleagues such as Tayfun Sönmez). Others will surely want to add their favorite successful economic theories.
How did these successes happen? And how did the economics profession turn successfully to empirical work informed by economic fables, as Noah Smith’s article discusses so well?
Answering these questions finally brings me to answer my previous ones. Fables are fantastical stories, yes. Yet, they can be sometimes matched to the infinitely messier world we observe around us, by setting up a correspondence between fable elements and aspects of reality, to allow us to make some good predictions and improve the fables by careful empirical testing. There is a classic paper by a physicist Eugene Wigner about the “unreasonable effectiveness of mathematics in the natural sciences“. Physicists were astonished by the effectiveness of mathematical fables in explaining the natural world around us. Social scientists have a much harder problem than physicists, studying large aggregations of human beings with all their agency and foibles, but they found fables useful too.
What makes the difference? How can a fable be useful in the social sciences, such as economics? It takes the talent of a good social scientist, coupled with hard-earned experience and knowledge of empirical methods, to find correspondences between the elements of a fable and observed social reality. The fables themselves do not provide instruction how to do this.
This is why I asked my students to read Rubinstein’s Economic Fables. I am convinced that you need economic fables, written in the powerful language of mathematics, to start being useful as an applied economist — the program in which I teach is designed to orient graduate students to applied economics in several fields. My colleagues can offer instruction on how to apply the fables better than I can, but I will teach some of the basic fables. I want the students to know just what these fables are for and what the limitations of fables are. This will be more useful for their careers in the long run than just the techniques (hard enough as mastering the techniques will prove in the next few months for my students).
So, dear students, I hope you manage to hold in your mind both the idea that economic models are fables (despite the implication some draw that they are useless because they are fables) and the idea that these fables are essential tools for any economist, but not the only essential tools. As you study, pay close attention to the assumptions in every model you encounter. (One of the best defenses of using mathematics extensively in economics is that it forces researchers to state their assumptions.) Think about how you would connect these assumptions with economic data that you can conceivably obtain. Go back and forth between fables and data. In Econometrics class, think of fables you can use to design your term paper. In Microeconomics (our class) and Macroeconomics, think about how you would subject the fables you are learning to scrutiny when you are finally finished with exams and start doing some independent research. In Mathematics for Economics, motivate yourself to persevere by remembering that you are amassing fable-building tools in a universal language that facilitates clear thinking and pushes back against intellectual dishonesty. Do these things, and your education will be professionally and personally useful and rewarding.
I would say to read this article and weep, but beyond the tears that you should have in your eyes about the horrible state of scientific publication and the terrible incentives it creates for scientists to always hunt exaggerated, “sexy” results, you can take a little comfort in this group that the article mentions that is trying to fight back. Now, if only senior scientists went along!
The death rate from drug overdoses in the US has reached the stratosphere. I am not a specialist in health economics, but I hope my colleagues who are may find some policy to propose to ameliorate this calamity.
In the Spring semester of 2017 I taught an undergraduate course on economic inequality for the first time. Every time I mention this to anyone, they want to know what I put in it. Now that I have a reasonable idea of what worked and what did not in the first outing of this course, I want to share here the basic structure of the course and some of the most important readings in it. To start with, here are the main topics, each with a short explanation and some reading suggestions. I regretted that there was no textbook for this course that presented the material I wanted to teach in the way I wanted to teach it; I have now embarked on writing such a book. (There does exist a huge Oxford Handbook of Economic Inequality, but it is not usable as a textbook, because it emphasizes breadth over depth of coverage and ends up with very brief discussions of difficult topics, that the reader has to work hard to assimilate by reading detailed expositions in the references.)
Course structure, including the main readings
- Normative reasons to care about economic inequality. You would think that why an economics student should care about inequality should be obvious to them, but I encountered, before I started the class, the question of why we should care. So I came up with this topic and the next. It didn’t hurt that I have long considered economists, in the main, much too narrow-minded regarding how to evaluate economic allocations from a normative point of view. The superb online Stanford Encyclopedia of Philosophy has an excellent article on distributive justice, with links to other articles on related topics. The Stanford Encyclopedia’s distributive justice article is a good core reading for this section of the course. The main temptation I had to stay away from when teaching this material was to include a serious survey of the voluminous work of the Nobel laureate economist Amartya K. Sen. This work deserves an entire semester for itself. Rather than extract some bits from it without all the context, I minimized references to it, with regret. For the interested reader, the following books by Sen contain a good presentation of his pathbreaking work:
Collective Choice and Social Welfare: An Expanded Edition, 2017;
The Idea of Justice, 2009;
Inequality Reexamined, 1995;
Choice, Welfare and Measurement, 1999.
There are more books and articles by Sen and his intellectual interlocutors that arguably belong to this list, but as the list is already too long, I am leaving it as is.
- Positive economic reasons to care about economic inequality. “Positive” here refers to “positive economics”, the study of “what is” in the economic sphere. In this usage, “positive” is contrasted with “normative”, the study of what ought to be, which was covered in the previous section with the discussion of distributive justice. This section focuses instead on a large experimental literature that makes up a large part of what is strangely called “behavioral economics”, the literature on the way individuals assess inequality in the way they evaluate economic outcomes. The main reading for this section is roughly the first half of this survey of the literature by Ernst Fehr and Klaus Schmidt. The main conclusion here is that individuals feel a lower level of satisfaction about economic conditions when there is more income inequality, all other things equal, even when they occupy a privileged position in the income distribution. This is not to say that everyone feels this way; rather, that enough people feel this way that force economists to study the topic of inequality because economic agents in the theories that economists build take account of inequality themselves in a way that affects their economic decisions.
- Equality of opportunity. When we are discussing economic opportunity, we usually find that even political conservatives mainly agree that individuals are responsible for their economic outcomes to the extent these follow from the individuals’ actions, but not from the individuals’ circumstances at birth that they have no control over (such as the individuals’ race). The intuitively clear way towards equality that this idea suggests is that if we equalize the starting point of everyone, then the inequality of economic outcomes that results from their actions is morally acceptable, as it results from their free agency. The main reading for this topic is the following difficult, but rewarding, article:
John E. Roemer and Alain Trannoy, Equality of Opportunity: Theory and Measurement, Journal of Economic Literature, 2016, 54(4), 1288‒1332.
There is plenty of opportunity to write a more accessible exposition of this topic, and it is part of my plan to do so.
- Measurement of inequality. Given a set of data on the distribution of some economic variable, such as income or wealth, across people, how do we come up with a numerical scale to tell is how unequal this distribution is? Several measures have been proposed, and some, like the Gini coefficient, are popular with researchers and writers in the field. An easily digestible exposition (but with a few typos in formulas) is in chapter 6 of:
Debraj Ray, Development Economics, Princeton University Press, 1998.
This set of lecture slides is a good complement to the chapter from Ray’s book, if nothing else, because it corrects the typos.
While we are discussing measurement, I simply must also point out the following online sources of data, in many cases very well presented with interactive graphics:
World Incomes Database: http://www.wid.world/
Chartbook on Economic Inequality: http://www.chartbookofeconomicinequality.com/
Income inequality from the Our World in Data website: https://ourworldindata.org/income-inequality/
Clearly a data-minded person could structure an entire semester’s course on the material from these three websites alone. I chose to put the material in a broader context, in which I present the data briefly and expect the students to use the data in their term papers, if they choose empirical term paper topics.
- Capital and inequality. This section is inevitable, given the enormous success of Piketty’s book Capital in the Twenty-First Century. The book became a surprise best-seller in the aftermath of the “occupy” years and there are several good critical discussions in the literature, including this excellent one by Lawrence Blume and Steven Durlauf:
Blume, Lawrence E. and Steven N. Durlauf, Capital in the Twenty-First Century: A Review Essay, Journal of Political Economy, 2015, vol. 123, no. 4, 749‒777. Available at https://www.ssc.wisc.edu/econ/Durlauf/includes/pdf/Blume%20Durlauf%20-%20Capital%20Review.pdf
Other good pieces in this debate include (I am trying hard to cite as few as possible):
Acemoglu, Daron and James A. Robinson, The Rise and Decline of General Laws of Capitalism, Journal of Economic Perspectives, 2015, 29(1), 3‒28, available at http://economics.mit.edu/files/11348, as well as Piketty’s own rejoinder to his critics,
Piketty, Thomas, Putting Distribution Back at the Center of Economics, Journal of Economic Perspectives, volume 29, number 1, Winter 2015, pages 67‒88.
- Globalization and inequality. In the aftermath of Brexit and the 2016 presidential election, I need not tell you why this is an issue of huge concern for the majority of voters in at least the UK and the US. The book by Branko Milanovic, Global Inequality: A New Approach for the Age of Globalization, is the central reading for this section, but one must not miss the critical discussion of this book here and the rejoinder by Milanovic and Lakner here. This debate is about Milanovic’s “elephant graph”, which became practically a meme online. This graph purports to show that the middle classes of rich societies have not kept up with income growth in Asian countries and among the rich in their own societies for a few decades now, hence the massive discontent about globalization one reads about in the press and finds reflected in election results.
- Technology and inequality. Robots are after our jobs, says practically everyone, or so it seems after only a few minutes of reading online about automation and its disruptive influence on work as traditionally understood. David Autor (yes, there is no “h” in his last name) has an accessible article that is the core reading for this section, in which he examines the impact of automation on employment and wage dispersion. The main lesson from this article is that the “middle-skill” occupations have been hit hard by automation, losing a lot of jobs, while jobs that need low or high skills have been treated well in terms of numbers of jobs by automation (but in terms of wages, only certain high-skill jobs have been treated well, but then these have been treated really, really well).
Autor, David H., Why Are There Still So Many Jobs? The History and Future of Workplace Automation, Journal of Economic Perspectives, volume 29, number 3, Summer 2015, pages 3-30.
Other readings for the ambitious reader who cares deeply about this topic include
Acemoglu, Daron and David H. Autor, Skills, Tasks and Technologies, from the Handbook of Labor Economics, http://economics.mit.edu/files/11635; also
Webber, Douglas A., Are college costs worth it? How ability, major, and debt affect the returns to schooling, Economics of Education Review, 2016, volume 53, pages 296-310.
For some of the latest on robots, see
Acemoglu, Daron and Pascual Restrepo, Robots and Jobs: Evidence from US Labor Markets, MIT Working Paper, March 2017.
- The impact of race and gender on inequality. There is a huge literature on the economics of discrimination. It is not possible to fit a detailed exposition of it in a small segment of a semester devoted to inequality. I found that the following two papers give enough of an introduction to these issues for anyone to pursue the topics further.
Lawrence Kahn, Wage Compression and the Gender Wage Gap, http://wol.iza.org/articles/wage-compression-and-gender-pay-gap/long;
Emmons, William R. and Noeth, Bryan J., “Race, Ethnicity and Wealth”, in “The Demographics of Wealth”, The Federal Reserve Bacnk of St. Louis, February 2015, https://www.stlouisfed.org/household-financial-stability/the-demographics-of-wealth.
- Economic policy against inequality. This topic is certainly one that students waited for eagerly while we were slogging through the theoretical and empirical context just detailed in bullet points 1 through 8. Parts two and three of Atkinson’s book Inequality have an excellent discussion of detailed policy proposals by Atkinson, whose death in the very beginning of 2017 deprived the field that studies economic inequality of one of its founders and intellectual giants. One topic that is bruited about a lot online is universal basic income. Right near the end of the semester the following book came out, devoted to this topic exclusively. It is a good book, and I plan to incorporate it better in future versions of the course, when I will have had time to prepare accordingly.
Philippe Van Parijs and Yannick Vanderborght, Basic Income: A Radical Proposal for a Free Society and a Sane Economy, Harvard University Press, 2017.
Even though I did not use a book as a textbook, I did refer the students to chapters in the following three books (which have been mentioned above). After teaching the course once, I decided that, although I will cite Piketty’s book among these three, I will only assign readings from the other two books.
- Atkinson, Anthony, Inequality: What Can Be Done? Harvard University Press, 2015.
- Milanovic, Branko, Global Inequality: A New Approach for the Age of Globalization, Belknap / Harvard, 2016.
- Piketty, Thomas, Capital in the Twenty-First Century, Belknap / Harvard, 2014.
Here is a magnificently done, tear-inducing recounting by Wil Wheaton about a horribly painful experience his wife had, an experience that she would have been spared if some male doctors who saw her early in that painful episode had been more attuned to the fact that woman’s body isn’t simply a “man’s body with some woman parts thrown in”.
I recently stumbled upon this article: How to Train Yourself to Be a More Rational Thinker, by Mark Hutson. I immediately started drafting this blog post, but I discovered that Hutson’s piece has so much excellent advice that summarizing it here would become an act of copying. Rather than do this, I just recommend you follow the link and read it for yourself, gentle reader. There is just one bit that I will quote verbatim, to whet your appetite. It comes from one of the more provocative and interesting thinkers I have encountered, Daniel Dennett, and it is quoted here from the Hutson article:
- You should attempt to re-express your target’s position so clearly, vividly, and fairly that your target says, “Thanks, I wish I’d thought of putting it that way.”
- You should list any points of agreement (especially if they are not matters of general or widespread agreement).
- You should mention anything you have learned from your target.
- Only then are you permitted to say so much as a word of rebuttal or criticism.
Not only will you conscript a more willing accomplice in your search for truth, but the exercise in itself will help you extract valuable material from the other side’s beliefs.
The Morris Arboretum in Philadelphia is exhibiting kinetic sculptures by Lyman Whitaker. I visited this afternoon and captured a few short videos. Here are two of them.
This article in the excellent microeconomic insights website summarizes in relatively simple language a recently published economics paper about the difficulties of regulating greenhouse gas emissions via market-based regional policies.