Super Crunchers by Ian Ayres Page A

sentences increased or decreased recidivism.
    The holy grail of criminologists has been to learn whether prison “hardens” or “rehabilitates” criminals. Does putting rapists away for ten instead of five years increase or decrease the chance that they’ll rape again when they’re back out on the street? This is an incredibly hard question to answer because the people we put away for ten years are different from those we put away for five years. Ten-year inmates might have a higher recidivism rate—not because prison hardened them, but because they were worse guys to begin with.
    Waldfogel’s randomization insight provided a way around this problem. Why not look at the recidivism rates of criminals sentenced by individual judges? Since the judges see the same types of criminals, differences in the judges’ recidivism rates must be attributable to disparities in the judges’ sentencing. Random assignment to (severe or lenient) judges is equivalent to randomly assigning criminals to longer or shorter sentences. Just as Waldfogel ranked business schools based on how well their students performed in the aftermarket, Waldfogel’s exploitation of randomization allows a ranking of judges based on how well their defendants perform in the post-prison aftermarket.
    So what’s the answer? Well, the best evidence is that neither side in the debate is right. Putting people in jail neither increases nor decreases the probability that they’ll commit a crime when they’re released. Brookings Institute economist Jeff Kling found that post-release earnings of people sentenced by the hanging judges were not statistically different from those sentenced by the judicial bleeding hearts. A convict’s earnings after prison are a pretty strong indicator of recidivism because people who are caught and put back in prison have zero taxable earnings. More recently, two political scientists, Danton Berube and Donald Green, have directly looked at the recidivism rates of those sentenced by judges with different sentencing propensities. Not only do they find that longer sentences incapacitate prisoners from committing crimes outside of prison, but also that the longer sentences of the hanging judges were not associated with increased or decreased recidivism rates once the prisoners hit the streets. The “lock ’em up” crowd can take solace in the fact that longer sentences are not hardening prisoners. Then again, the longer sentences don’t specifically deter future bad acts. Because of randomized assignments, we might start changing the debate about sentencing length from questions about specific deterrence and rehabilitation, and instead ask whether longer sentences deter other people from committing crime or whether simply incapacitating bad guys makes longer sentences worthwhile.
    But the big takeaway here concerns the possibility of piggybacking. Instead of randomly intervening to create data, it is sometimes possible to piggyback on pre-existing randomization. That’s what criminologists are doing with regard to random judicial assignments. And it’s what I’ve started to do with regard to random assignments at our local school district. About 20 percent of New Haven schoolchildren apply to attend oversubscribed magnet schools. For the schools that are oversubscribed, the kids are chosen by lottery. Can you see what piggybacking will let me do? I can look at all the kids who applied to Amistad Academy and then compare the average test scores of those who got in and those who didn’t. Piggybacking on randomization provides me with the kind of Super Crunching information that will allow me to rank the value added of just about every school in the district.
    The World of Chance
    The randomized testing of social policy is truly now a global phenomenon. Dozens upon dozens of regulatory tests have been completed in every corner of the globe. Indeed, if anything,