by The limits of DAG formalism There are good reasons to think that moderating causes have an important role general in explaining development and growth. Why? The growth process is apparently strongly affected by what economists call complementarities. Complementarities exist when the action of an agent or the existence of practice affects the marginal benefit to another agent taking an action or to the marginal benefit of another practice. Education is again a good example.A well-trained workforce promotes high value-added production and the existence of the latter provides incentives for educational attainment. The influence of either on growth depends on the value of the other. Arguably, there are complementarities across the board for the factors that
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The limits of DAG formalism
There are good reasons to think that moderating causes have an important role general in explaining development and growth. Why? The growth process is apparently strongly affected by what economists call complementarities. Complementarities exist when the action of an agent or the existence of practice affects the marginal benefit to another agent taking an action or to the marginal benefit of another practice. Education is again a good example.A well-trained workforce promotes high value-added production and the existence of the latter provides incentives for educational attainment. The influence of either on growth depends on the value of the other. Arguably, there are complementarities across the board for the factors that matter for
development. Other examples besides human capital include market size and the division of labor, and financial development and investment. Complementarities create the kind of contextual effects characteristic of what I have called complex causality.
I am not claiming that using explicit DAGs and explicitly testing them in development economics or elsewhere is a bad thing; quite the opposite. It is a significant improvement over the standard practice of uninterpreted regressions. Nor am I claiming the problems I am pointing to are completely unapproachable in the causal modeling framework. For example, samples can be divided along a moderator variable and separate DAGs tested, with differences being evidence for effect modification. My concern, however, is that the DAG formalism not become a hammer where everything is a nail.