statistics vs. econometrics – heckman’s approach
Over at Econ Talk, Russ Roberts interviews James Heckman about censored data and other statistical issues. At one point, Roberts asks Heckman what he thinks of the current identification fad in economics (my phrasing). Heckman has a few insightful responses. One is that a lot of the “new methods” – experiments, instrumental variables, etc. are not new at all. Also, experiments need to be done with care and the results need to be properly contextualized. A lot of economists and identification obsessed folks think that “the facts speak for themselves.” Not true. Supposedly clean experiments can be understand in the wrong way.
For me, the most interesting section of the interview is when Heckman makes a distinction between statistics and econometrics. Here’s his example:
- Identification – statistics, not economics. The point of identification is to ensure that your correlation is not attributable to an unobserved variable. This is either a mathematical point (IV) or a feature of research design (RCT). There is nothing economic about identification in the sense that you need to understand human decision making in order to carry out identification.
In contrast, he thought that “real” econometrics was about using economics to guide statistical modelling or using statistical modelling to plausibly tell us how economic principles play out in real world situations. This, I think, is the spirit of structural econometrics, which demands the researcher define the economic relation between variables and use that as a constraint in statistical estimation. Heckman and Roberts discuss minimum wage studies, where the statistical point is clear (raising wages do not always decrease unemployment) but the economic point still needs to be teased out (moderate wage increases can be offset by firms in others ways) using theory and knowledge of labor markets.
The deeper point I took away from the exchange is that long term progress in knowledge is not generated by a single method, but rather through careful data collection and knowledge of social context. The academic profession may reward clever identification strategies and they are useful, but that can lead to bizarre papers when the authors shift from economic thinking to an obsession with unobserved variables.