What Should I Teach First-years?

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

I’m doing a little post-mortem on this semesters first-year graduate macro class. I’m thinking about what I should be teaching in this course. The big meta-question is what is the right kind of material to be teaching? I see two perspectives here:

  • Teach the big questions. They need to understand the open issues for macroeconomics, and then can be introduced in 2nd/3rd year to models/techniques that are suited to talking about those issues. A course like this would emphasize intellectual history more than specific mathematical techniques. The problem is that we don’t necessarily screen out people who cannot do the math at a sufficiently advanced level to try and *answer* any interesting questions.
  • Teach the techniques. My course, and most 1st-year courses, lean heavily this way. Once they have some of the “language” down, then we can talk coherently about the big questions with them in the 2nd/3rd year. The problem with this course is that we don’t necessarily screen out people who cannot understand what it means to ask an interesting question. The danger is we get optimization robots, not researchers.

Maybe I should just trust that PhD programs have evolved towards the right solution, and focus on techniques. The cost of having someone incapable of using techniques is so high later on that it must be avoided at all costs. But there is a part of me that feels like techniques are always something that can be learned by force of effort later on. Screening out people who can’t think without being given a specific math problem to do might be more useful.

Of course, if one does want to teach “big questions” to first-years, what are they?

For people who’ve done PhD’s, or are doing them now. What do you *wish* you had learned in first-year macro. What would have been useful?

I am far too lazy to try and think of this all by myself, so I’m posting it here in the hopes that smart people will offer up some suggestions either way. Any ideas are appreciated, will be stolen without shame, and will probably sit unused for years as a scribbled note to myself under a pile of other things on my desk.

Cochrane on Growth and Macro

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

John Cochrane recently ran a little review of his experience at NBER (h/t to Noah Smith). It’s got a really interesting observation on growth versus macro.

A last thought. Economic Fluctuations merged with Growth in the mid 1990s. At the time there was a great confluence of method as well as interest. Growth theorists were studying growth with Bellman equations, dynamic general equilibrium models of innovation and transmission of ideas, thinking about where productivity shocks came from. Macroeconomists were using Bellman equations, and studying dynamic general equilibrium models with stochastic technology, along with various frictions and other propagation mechanisms.

That confluence has now diverged. I enjoyed spending an hour or two thinking about how religion has blocked or adapted to ideas over the centuries, and Paul’s view on social norms or neuroeconomics. But I don’t really have any expertise to contribute to that debate. Questions like whether young CEOs head more innovative companies, or whether, like deans, what matters is the age of the faculty are a little closer to home, since I spend a lot of time consuming corporate finance. But the average sticky-price macro type does not. Likewise, when Daron Acemoglu, who seems to know everything about everything, has to preface his comments on macro papers with repeated disclaimers of lack of expertise, it’s clear that the two fields really have gone their separate ways. Perhaps it’s time to merge fluctuations with finance, where we seem to be talking about the same issues and using the same methods, and growth to merge with institutions and political or social economics.

This is similar in flavor to John Seater’s comment that I wrote about here. Has growth economics become different enough from mainstream macro that we should separate them from one another?

I’d argue yes. Growth is about development now – meaning that it’s motivating question is “Why are some countries rich and some poor?”. (See my earlier post on this topic here). The exploration of answers to this question are much more about big static differences in institutions, cultures, technologies, and the like, and less about transition paths and dynamics.

On what growth would look like if it did separate (literally at NBER and intellectually as a field) from macro, Cochrane gave us perhaps a pointer:

I’m not sure in the end though whether Paul[Romer] was approving or bemoaning the shift back towards literature in economic analysis. Certainly his vision for the future of growth theory, centered on values, social norms, biology, and so forth, does not lend itself easily to quantification.

Is this a feature or a bug? Perhaps the big question of “Why are some countries rich and some poor?” is not answerable in any solid empirical way. Perhaps the highest achievement here is “literature” in the sense of some overarching theory that one uses to examine history. Think of Pomeranz’s The Great Divergence or Robert Allen’s The British Industrial Revolution in Global Perspective as examples. While both books certainly appeal to economic intuition and occasionally something approaching formal theory, neither considers anything like a Bellman equation.

The counter would be that we can do better than just “literature” in growth by writing down model (perhaps static models, but no matter) that allow us to quantify the forces that people like Pomeranz and Allen propose as relevant. That is, write down an explicit model, and calibrate or simulate it to assess whether a proposed explanation has a plausibly large quantitative effect on output per worker. The issue here is, as Cochrane says, it’s essentially impossible to quantify religion or values. What is the parameter you stick in your quantitative model that captures the effect of a belief in the afterlife on your willingness to work today? If you cannot possibly hope to measure that parameter, then you cannot quantify it’s effect on output per worker.

So if we’ve entered the world where we think that values (or culture or religion) are fundamental to development, then we may be left with “literature” as the only valid form of research output. My guess is that growth economists will resist this kind of transition, mainly because we’ve invested a lot in knowing fancy dynamic models and calibration techniques, and we don’t want those skills to become worthless.

Potential “Potential Output” Levels

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

John Fernald has a new working paper out at the San Fran Fed on “Productivity and Potential Output Before, During, and After the Great Recession”. The main take-away from the paper is that productivity growth started to slow down even before 2008, particularly in industries that produce IT products or are significant users of IT products. Because of this, even in the absence of the Great Recession, we would have seen slower trend growth in GDP.

What Fernald’s results imply is that the economy is not as far from its potential GDP as we might think. And the idea that we’re way below potential GDP is something lingering underneath a lot of the discussion about economic policy (tapering, stimulus, etc…). Matt Yglesias just had a post noting that while the U.S. is well below it’s pre-2007 trend for GDP, Europe is even farther below it’s trend. Regardless of the conclusion you want to draw from that regarding policy, the assumption is that the pre-2007 trend is where GDP “should” be.

Back to Fernald’s paper. He finds that productivity growth was already declining prior to 2007, and therefore where GDP “should” be is a lot lower than the naive pre-2007 trend line would indicate. This is easier to see in a picture.
Fernald (2014) Potential GDP
The purple dashed line is from the CBO’s 2007 projection, and that is essentially just an extrapolation of the trend in GDP from about 1990-2007. Compared to that measure of potential GDP, we are doing very poorly, with actual GDP (the black line) falling nearly $2 trillion short of potential.

Fernald’s alternative calculations that take into account the slowdown in productivity growth that started in the mid-2000’s suggest a much lower estimate of potential GDP. His estimate (the red line) is a prediction of what GDP would have been without the financial crisis, essentially. It falls well below the CBO 2007 estimate. It suggests that the economy today is only perhaps $400 billion short of potential GDP.

His numbers make a big difference in how you think about policy, if only at the quantitative level. If you’re for some kind of further monetary expansion or a new fiscal stimulus, then the size of that boost should be calibrated to a $400 billion shortfall, not a $2 trillion one.

Why does Fernald come up with lower numbers for potential output than the naive forecast in 2007? Without going into the nitty-gritty, he looks at productivity growth (think output per hour) and finds that around 2003Q4, it stops growing as quickly as it did from 1995-2003. What Fernald chalks this up to is the exhaustion of the IT productivity boost. At the time, people thought that the IT revolution might have permanently raised labor productivity growth rates It appears to rather have had a “level effect” – we had a boost in the level of labor productivity, but now it will continue to grow at the normal rate. Again, this is easier to see in pictures, courtesy of Fernald’s paper.
Fernald (2014) productivity trends

You can see that the 1995-2003 period is exceptional in having high labor productivity growth, and that since 2003 we’ve had growth in labor productivity at about the same rate as 1973-95. Anyone who uses the 1995-2003 period to extrapolate labor productivity growth (like the CBO was implicitly doing in 2007) would overestimate potential output.

This isn’t to say that the CBO or anyone else was being lazy or duplicitous. In 2007, if you looked at the data on labor productivity, there would not be enough evidence to suggest that growth in labor productivity had fallen. The data from 1995-2007 would not be enough to tell you if we had experienced a “level effect” from IT that led to a temporary boost to growth rates, or a “growth effect” from IT that had permanently raised growth rates. You can only tell the difference now because we see the slowdown in productivity growth, so in retrospect it must have been a “level effect”.

Regardless, Fernald’s paper suggests that the scope of the Great Recession is less “Great” than previous estimates would lead you to believe. And given that the trend growth rate in labor productivity is driven primarily by technological innovation, then boosting that growth rate means hoping that someone will invent a new technology that has a transformative power similar to IT.

Defining Development Economics

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

Trying to precisely define an area of study is impossible, but thinking through the definition of “development economics” is an interesting diversion. As with most fields, the definition is tied closely to the people doing research in that area. So today, “development economics” is the kind of research done by people like Esther Duflo, Ted Miguel, Michael Kremer, and a group of other very smart people who I will offend by not mentioning here. This kind of development economics has several key features. (1) It very consciously takes place in developing countries. These researchers are out collecting surveys or doing studies “in the field”. Perhaps the best way to define a development economist today is as someone whose presentation includes a picture of the village they worked in to collect data. (2) It is intensely concerned with identification of causal effects. Thus this field aspires to do randomized control trials (RCTs) to identify the causal effect of some {X} (e.g. de-worming treatments) on some {Y} (e.g. school attendance), as in Kremer and Miguel (2004). Failing that, some kind of natural experiment that features quasi-random treatment effects is examined. (3) It tends to be a-theoretical. The RCTs are showing reduced-form empirical effects of some kind of treatment on some kind of outcome. The de-worming paper of Kremer and Miguel is purely empirical, for example. This isn’t generally true, as there are papers that explicitly are testing some theory, but the dominant portion of the literature is purely empirical.

Through some historical inertia in the profession, we call this research “development economics”. But I think that this type of research is more properly called “poverty economics”, the study of individuals living in particularly poor, under-developed countries. Duflo and Banerjee‘s 2011 book is actually called Poor Economics, and the tag-line is A Radical Rethinking of the Way to Fight Global Poverty. The focus is on alleviating the conditions of extreme poverty: poor health, poor nutrition, and low education. The RCTs are evaluations of interventions that aim to improve health, or nutrition, or educational attainment. By going out into these developing countries, these researchers are acutely aware of the constraints facing poor people, and are studying ways to alleviate those constraints.

This is all valuable research. It is perhaps more admirable in its motivations than other sub-fields of economics (*cough* finance *cough*). But it is not about “development”.

Economic development is about the transition of whole economies from low-productivity, poor places into high-productivity industrial economies. This transition encompasses several aspects: a move out of agriculture and into manufacturing or services, urbanization, declining fertility rates, integration with global markets. Current research in development economics – the RCTs and their like – does not study the transition. “What will make these people better off today?” is a different question than “What will make this economy develop?”.

If you go back far enough in the development literature, you’ll find that the second question is the dominant one. Lewis, Nurske, Rosenstein-Rodan, Boserup, Gerschenkron, Hirschman all were concerned with what drove the transition to high-productivity industrial economies. But while they focused on this broader question, their work also contained assumptions that steered the profession away towards “poverty economics”. An (often unspoken) assumption of much of this early development work was that rural peasants were irrational. That is, they did not respond to prices or incentives the way that people in modern economies did. They were tradition-bound, stuck in their ways. Development meant breaking this resistance to change and educating them to operate in a market-based economy.

The reaction to this, most notably associated with T. W. Schultz’s 1964 book on Transforming Traditional Agriculture, was that peasants were in fact rational, but faced a unique set of constraints. If they stuck to traditional means for organizing production, then that is because those traditions were solving some concrete problem. Perhaps the best example is share-cropping, which even Alfred Marshall critiqued for being inefficient. It seems that by share-cropping, the marginal return for the peasant of more labor or capital is lowered, and hence less effort or investment is put forth. To the early development economists, share-cropping was an example of a traditional institution that prevented higher output. Except that once you appreciate the uncertainty involved in family farming, it is possible that share-cropping is the optimal contract to pick because it shares risk between the land-owner and farmer.

This led development economists on a different heading. The hunt was on for reasonable explanations of the observed behavior in these underdeveloped villages. What were the constraints or conditions that prevented these peasants from making more investments, or adopting better technology? This led to all kinds of seminal insights. Joseph Stiglitz, as one example, cites his time in Kenya in the late 60’s as pivotal for developing his ideas on the economics of information.

However, in pursuing this line of thinking, development economics got so deep into the details of optimizing behavior in under-developed villages that it lost track of the larger question: “What will make this economy develop?”. The study of the nuances of under-developed markets became an end in itself. A concrete example is the survey by Otsuka, Chuma, and Hayami (1992, JEL, sorry no link) on “Land and Labor Contracts in Agrarian Economies”. They say, “Through a critical review of the existing studies of agrarian contracts, this essay points towards building a `general model’ in which land tenancy, labor employment, and owner cultivation are modeled together as substitutes along a continuous spectrum of contract choice.”. And it is a very nice synthesis of the literature in this area to that point. But what implications does it have for development? What does this general model tell us about how or why a country will make the transition into an industrial economy? Knowing that peasants are rational rather than irrational is great, but I still would like to know how those peasants’ kids or grandkids will (or will not) end up as machinists or office workers living in city one day.

This is where development economics started turning into poverty economics. The focus became purely on understanding the constraints facing poor people in under-developed countries. As these constraints were dire, and led to such bad outcomes (poor health, low education, etc..), alleviating those constraints became the first-order concern. And let’s be clear, it is very much a first-order concern. Millions of people dying from an easily preventable disease is a travesty. Running RCTs to establish the best way to distribute that treatment is incredibly valuable research.

Despite that, current development economics doesn’t address the broader questions, the older questions, of what drives development in the long-run. The field of growth economics has essentially adopted this set of questions as part of its own research agenda. One of the things that this “macro-development” research does is establish the aggregate impact of micro-level features of under-developed economies. Does a given micro-level distortion or constraint incur such costs that it is a material reason for why a country remains relatively poor? Two recent examples are Hsieh and Klenow‘s 2009 paper on the aggregate effects of misallocations across firms in China and India, or Lagakos and Waugh‘s 2013 model of selection and cross-country income differences.

This doesn’t make the growth/macro-development approach better or worse than poverty economics. The two fields are just looking at different questions, with different implications. It’s worth keeping that in mind when evaluating the research in the two fields. In particular, it is not helpful to criticize one literature with the tools of the other (e.g. “But how do you plan on getting identification of this effect?” or “But who cares about the reduced form effect? What’s the mechanism you think is at work here?”). Different questions, different approaches, different techniques.