My recent reading of David Colander and Roland Kupers’s Complexity and the Art of Public Policy prompted me to re-read James Manzi’s Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society. I see the two books as riffs on a similar theme.
I’ll post a review of Uncontrolled later this week, but in the meantime, Manzi provides an interesting take on the Donohue-Levitt abortion-crime hypothesis. Their hypothesis is that abortion reduces crime as unwanted children are more likely to become criminals. As the legalisation of abortion increased access to abortion and decreased the number of unwanted children, decreases in crime through the 1990s and 2000s could be due to this legalisation.
Donohue and Levitt’s initial paper triggered a raft of responses, including one demonstrating an analytical error, which, once corrected for, resulted in the abortion-crime link disappearing. Donohue and Levitt then redid the work, and showed by recasting a few assumptions, the error could be corrected for and the link re-established. As Manzi states:
The revealing observation is not that there was an analytical error in the paper (which almost certainly happens far more often than we like to think), but that once it was found and corrected, it was feasible to rejigger the regression analysis to get back to the original directional result through various defensible tweaks to assumptions. If one could rule out either the original assumptions or these new assumptions as unreasonable, that would be better news for the technique. Instead we have a recipe for irresolvable debate.
Manzi also points out that Levitt, in his book Freakonomics (with Stephen Dubner), indirectly identified one of the reasons why Donohue and Levitt’s claim is so tenuous:
In Freakonomics, Levitt and Dubner write that Roe [the Supreme Court decision in Roe v Wade establishing a right to abortion] is “like the proverbial butterfly that flaps its wings on one continent and eventually creates a hurricane on another.” But this simile cuts both ways. It is presumably meant to evoke the “butterfly effect”: meteorologist Edward Lorenz’s famous description of a global climate system with such a dense web of interconnected pathways of causation that long-term weather forecasting is a fool’s errand. The actual event that inspired this observation was that, one day in 1961, Lorenz entered .506 instead of .506127 for one parameter in a climate-forecasting model and discovered that it produced a wildly different long-term weather forecast. This is, of course, directly analogous to what we see in the abortion-crime debate and Bartels’s model for income inequality: tiny changes in assumptions yield vastly different results. It is a telltale sign that human society is far too complicated to yield to the analytical tools that nonexperimental social science brings to bear. The questions addressed by social science typically have none of the characteristics that made causal attribution in the smoking–lung cancer case practical.