Teaching Economics 101 Amidst the Empirical Revolution

Noah Smith writes in Bloomberg View, most of what you learned in economics 101 is wrong:

In the last three decades, the economics profession has undergone a profound shift. The rise of information technology and new statistical methods has dramatically increased the importance of data and empirics. This means that many professional economists are no longer, as empirical pioneer David Card put it, “mathematical philosophers.” Instead, they are more like scientists, digging through mountains of evidence to find precious grains of truth.

Noah cites examples where traditional Economics 101 theory states one thing, while modern empirical studies state the opposite.

First, in labor economics regarding minimum wages. Economics 101 theory says minimum wages (or price floors, in general) will quickly put a group of low-wage workers out of a job. Empirical studies shed some doubt on the reliability of this theory when they fail to find the predicted loss of employment.

Second, in welfare economics regarding social programs. Economics 101 theory says welfare makes people lazy because it subsidizes leisure relative to work. Empirical studies (from around the world) suggest precisely the opposite, that recipients of social support may actually work more, on average.

I have a couple (half-baked) thoughts:

1. I question whether empirics necessarily crowds out theory. To be clear: I lean toward the empirical side of economic analysis. I’m a graduate student in an applied department and my field of specialization (international development) is an applied field within the discipline. Generally speaking, I’m much more convinced by rigorous empirical analysis than an elegant theoretical model. This being said, however, theory plays a critical role in formulating how we (economists) think about the world and how we (empirical economists) design and implement empirical studies. Without a solid foundation of economic theory it would be difficult (maybe impossible) to perform rigorous empirical analysis. While the discipline of economics is indeed experiencing an empirical revolution, I am not the first to predict that the pendulum will swing back toward theory in the near future. The best work in the future (I think) will include both precise theoretical modeling and rigorous empirical analysis.

2. I have a (not so original) idea about teaching traditional economics 101 theory in a data-rich world. While most of what is taught in economics 101 might (strictly speaking) be wrong, I’m not convinced that the content of the typical course curriculum should be changed all that much. The idea of labor market equilibrium and work-leisure trade-offs are both core tenants of the discipline. It would be hard to conceptualize an interesting and worth-while empirical study without an understanding of these basic, simple, and yes probably wrong theories. Additionally, it seems to be that attempting to teach econometrics to undergraduates prior to teaching them economics 101 (even using the excellent Mastering ‘Metrics) would be difficult (if not totally ineffective).

What should change, however, is how the theories of economics 101 are presented to young students. Rather than teaching students that minimum wages will cause unemployment to rise or that welfare programs will make recipients lazy, teach the theories with more nuance. Such as: one way of thinking about the impact of a minimum wage regulation is that it could cause unemployment to rise, although there are many other factors at play and we are generally unsure as to the net effects. Same with welfare programs: one way of thinking about the impact of programs like TANF or WIC (in the US) or cash transfers or agricultural input subsidy programs (in many African countries) is that these programs could cause recipients to work less, although there are many other factors at play and we are generally unsure as to the net effects.

Ultimately, it comes down to a simple yet profound lesson from Dani Rodrik’s newest book: Never make the mistake of confusing a model of how the world works with the model of how the world works.