Simpson’s paradox and perspectival realism

15 September, 2015 at 13:23 | Posted in Economics | 1 Comment

Which causal relationships we see depend on which model we use and its conceptual/causal articulation; which model is bestdepends on our purposes and pragmatic interests.

simpsons_paradox_by_insecondsflat-d37lk7yTake the case of Simpson’s paradox, which can be described as the situation in which conditional probabilities (often related to causal relations) are opposite for subpopulations than for the whole population. Let academic salaries be higher for economists than for sociologists, and let salaries within each group be higher for women than for men. But let there be twice as many men than women in economics and twice as many women than men in sociology. By construction, the average salary of women is higher than that for men in each group; yet, for the right values of the different salaries, women are paid less on average, taking both groups together. [Example: Economics — 2 men earn 100$, 1 woman 101$; Sociology — 1 man earn 90$, 2 women 91$. Average female earning: (101 + 2×91)/3 = 94.3; Average male earning: (2×100 + 90)/3 = 96.6 — LPS]

An aggregate model leads to the conclusion that that being female causes a lower salary. We might feel an uneasiness with such a model, since I have already filled in the details that show more precisely why the result comes about. The temptation is to say that the aggregate model shows that being female apparently causes lower salaries; but the more refined description of a disaggregated model shows that really being female causes higher salaries. A true paradox, however, is not a contradiction, but a seeming contradiction. Another way to look at it is to say that the aggregate model is really true at that level of aggregation and is useful for policy and that equally true more disaggregated model gives an explanation of the mechanism behind the true aggregate model.

It is not wrong to take an aggregate perspective and to say that being female causes a lower salary. We may not have access to the refined description. Even if we do, we may as matter of policy think (a) that the choice of field is not susceptible to useful policy intervention, and (b) that our goal is to equalize income by sex and not to enforce equality of rates of pay. That we may not believe the factual claim of (a) nor subscribe to the normative end of (b) is immaterial. The point is that that they mark out a perspective in which the aggregate model suits both our purposes and the facts: it tells the truth as seen from a particular perspective.

Kevin Hoover

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  1. Hoover’s example is a bit facile. Once you know that disaggregation is significant, then you cannot simply use the aggregated data as another viewpoint. That is, you have to keep the interaction between fields and gender in mind. That is not the same as “tak[ing] an aggregate perspective”, as Hoover puts it

    In his example, within each field women are paid more than men. Thus, if there is a bias against women, it must have to do with more women being in the lower paying field. One historical example in this regard is bank tellers. At a time when school teachers were largely women (school marms), bank tellers were exclusively male, or almost so. Later on, bank tellers became largely women. There is certainly a case that this happened, not because more women than men prefer to be tellers, but because, as over time more women entered the work force, women were relegated to lower paying jobs.


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