Is economics a science?
These slides don't speak for themselves quite as much as the macro slides did, and the topic is much broader and more vague, so I'll turn it into a full post. This post mostly just explains the slides.
What the heck is a "science"?
No one knows. Because no one has ever really been able to make one dominant definition of science stick. Some people define it as a method (e.g. Popper), some as a sociological phenomenon (Kuhn, Lakatos), and others don't even see much need for a definition (Feyerabend). And there are plenty of other opinions too.
So the argument over whether economics is "a science" will never be resolved.
It certainly isn't a lab science; though econ experiments are interesting and can be helpful, they lack ecological validity - i.e., what we really want to know is how the big, messy, complex world works in practice. So lab experiments by themselves aren't going to get the job done, or even come close.
Therefore, if econ is to be a "science", it has to be a largely empirical "science". And since empirical research and lab research are fundamentally different ways of understanding the world, that means some people will always say econ isn't a science. But if you accept that empirics can be "scientific", then econ has a chance.
Anyway, I do think there are three trends in economics that most people will agree are making it more scientific:
1. Theories with strong predictive power
2. Less theory, more empirical work
3. The "credibility revolution" in empirical economics
Theories that work
Lots of people claim that social science can't be a "science", because human beings don't obey precise mathematical laws. That just seems silly to me. First of all, plenty of natural sciences don't involve precise mathematical laws - what's the mathematical law describing how food passes through the digestive tract? Second of all, there are plenty of social science theories that are written in quantitative form - equations, numbers, and all that - and that have consistent ability to predict human behavior.
In the slides I give four examples from economics. These are:
1. Auction theory
2. Matching theory
3. Discrete choice models
4. Gravity trade models
This is an eclectic mix of theories. One is explicitly neoclassical (discrete choice), while another relies on individual rationality (auction theory). One is basically an algorithm for central planning (matching), which makes it a close cousin to lots of models in operations research (which, by the way, also frequently are able to predict human behavior with quantitative precision). And one, the gravity trade model, is a "big" theory that successfully predicts patterns of international trade involving billions of individuals.
So the idea that social science can't predict human behavior is pretty conclusively disproven, just by these four examples. There are, of course, many more such examples, most of which are probably not from econ.
But the fact that econ is making progress on this front is encouraging. Slowly, the discipline is building up a stable of models with good, reliable predictive power.
In fact, although it's a bit prosaic and boring, even the good old Econ 101 supply-and-demand model probably works great for some things. It doesn't work for labor markets, but I bet it can fairly accurately predict the effect of a Florida hurricane on orange prices.
Anyway, theories that work, and which have engineering applications beyond the halls of academia, are generally considered to be a hallmark of "real science," and people ought to know that econ is getting more and more of these theories.
The empirical revolution and the credibility revolution
In recent years, economics has become much more empirical - theory papers used to represent almost two thirds of what got published in top journals, and as of 2011 they had fallen to just over one quarter. The stereotype that economists are "mathematical philosophers" who just sit around and make theories all day is less and less true.
Meanwhile, the "credibility revolution" - i.e., the rise of quasi-experimental methods - is rapidly increasing the direct real-world applicability of empirical economics. Instead of having to use dubious structural theories as an intermediary, economics papers are cutting right to the chase - finding believable estimates of the effects of policies like minimum wage and immigration.
This is having big real-world impacts. Minimum wage studies since the 1990s have found few short-term disemployment effects, which probably helped inform the decisions of a number of cities to increase their minimum wages to $15 in recent years. So far, studies of these new measures have agreed with the earlier results - there hasn't been much disemployment.
So the rise of quasi-experiments is important and good. However, it's important to recognize the limitations of this approach. Quasi-experiments only give us local understanding of the world, not the kind of global understanding that we'd need for really big bold policy moves. In the long run, being able to deeply understand the economy will require working structural models.
The second problem is that quasi-experiments usually must be found by luck rather than purposefully implemented, and even the ones that are purposefully implemented (lotteries, RCTs) are limited in the set of things they can study. This leads to the so-called "lamppost problem," in which easy-to-study things get studied and hard-to-study things get discounted. For example, studies pretty conclusively show that the minimum wage doesn't destroy many jobs in the short run, but the idea that minimum wage constrains job growth over the long run is much harder to study using quasi-experiments. Harder to study, but still important for policy.
So although quasi-experimental results are great and the shift to empiricism is welcome, it's important to keep working on theory as well.
Ways econ could stand to be more scientific
Though it's made progress in the aforementioned areas, econ still has a number of ways it could be more "science-y".
First and foremost, econ needs to get more comfortable with the idea that data can actually kill theories. This is pretty widely regarded as a hallmark of true science - theories can't just be pure assumptions or axioms, they have to be disciplined by data. And adding bells and whistles to patch up theories only gets you so far - at some point, you have to be willing to say "Well, that theory is just wrong," and try something else.
Currently, economists in general are extremely reluctant to toss out any theories at all. Even simple, elementary theories with plausible replacements get excused and excused when they contradict the evidence. A good example is the "Econ 101" theory of labor markets - supply-and-demand might work great for the market for oranges, but it fails pretty catastrophically as a description for the aggregated labor market. Yet this model is still in extremely wide use, both formally and informally.
There are a number of other, less glaring ways that econ continues to over-privilege theory. Paul Pfleiderer notes the prevalence of "chameleon" models that are sold as unrealistic thought experiments but then used by policymakers to support their desired conclusions. Ricardo Reis laments the fact that young economists are forced to insert pointless theory sections into their empirical papers. Econ Nobel prizes are given for developing new methodologies, even if those methodologies haven't yet yielded much in the way of predictive success. And as I noted in my macro presentation, many models continue to include standard elements that are flatly contradicted by the data.
So in order to become yet more scientific, econ needs to stop putting theory on a pedestal. Instead of separating the worlds of theory and empirics, economists should insist that the two follow the same back-and-forth relation that they do in the natural sciences. Theories need to be allowed to fail when measured against data, and data needs to be used to construct new, better theories.
In conclusion
Here's the final slide from the presentation, which I think sums things up quite nicely:
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