by What has always bothered me about the “experimentalist” school is the false sense of certainty it conveys. The basic idea is that if we have a “really good instrument” we can come up with “convincing” estimates of “causal effects” that are not “too sensitive to assumptions.” Elsewhere I have written an extensive critique of this experimentalist perspective, arguing it presents a false panacea, andthat allstatistical inference relies on some untestable assumptions … Consider Angrist and Lavy (1999), who estimate the effect of class size on student performance by exploiting variation induced by legal limits. It works like this: Let’s say a law prevents class size from exceeding. Let’s further assume a particular school has student cohorts that average about 90, but that
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What has always bothered me about the “experimentalist” school is the false sense of certainty it conveys. The basic idea is that if we have a “really good instrument” we can come up with “convincing” estimates of “causal effects” that are not “too sensitive to assumptions.” Elsewhere I have written an extensive critique of this experimentalist perspective, arguing it presents a false panacea, andthat allstatistical inference relies on some untestable assumptions …
Consider Angrist and Lavy (1999), who estimate the effect of class size on student performance by exploiting variation induced by legal limits. It works like this: Let’s say a law prevents class size from exceeding. Let’s further assume a particular school has student cohorts that average about 90, but that cohort size fluctuates between, say, 84 and 96. So, if cohort size is 91–96 we end up with four classrooms of size 22 to 24, while if cohort size is 85–90 we end up with three classrooms of size 28 to 30. By comparing test outcomes between students who are randomly assigned to the small vs. large classes (based on their exogenous birth timing), we obtain a credible estimate of the effect of class size on academic performance. Their answer is that a ten-student reduction raises scores by about 0.2 to 0.3 standard deviations.
This example shares a common characteristic of natural experiment studies, which I think accounts for much of their popularity: At first blush, the results do seem incredibly persuasive. But if you think for awhile, you start to see they rest on a host of assumptions. For example, what if schools that perform well attract more students? In this case, incoming cohort sizes are not random, and the whole logic beaks down. What if parents who care most about education respond to large class sizes by sending their kids to a different school? What if teachers assigned to the extra classes offered in high enrollment years are not a random sample of all teachers?
Keane’s critique of econometric ‘experimentalists’ gives a fair picture of some of the unfounded and exaggerated claims put forward in many econometric natural experiment studies. But — much of the critique really applies to econometrics in general, including the kind of ‘structural’ econometrics Keane himself favours!
The processes that generate socio-economic data in the real world cannot just be assumed to always be adequately captured by a probability measure. And, so, it cannot be maintained that it even should be mandatory to treat observations and data — whether cross-section, time series or panel data — as events generated by some probability model. The important activities of most economic agents do not usually include throwing dice or spinning roulette wheels. Data-generating processes — at least outside of nomological machines like dice and roulette wheels — are not self-evidently best modelled with probability measures.
When economists and econometricians — often uncritically and without arguments — simply assume that one can apply probability distributions from statistical theory to their own area of research, they are skating on thin ice. If you cannot show that data satisfies all the conditions of the probabilistic ‘nomological machine,’ then the statistical inferences made in mainstream economics lack sound foundations.
Statistical — and econometric — patterns should never be seen as anything other than possible clues to follow. Behind observable data, there are real structures and mechanisms operating, things that are — if we really want to understand, explain and (possibly) predict things in the real world — more important to get hold of than to simply correlate and regress observable variables.
Statistics cannot establish the truth value of a fact. Never has. Never will.