Among the many fascinating contributions in the latest contribution to the popular literature on economics is a chapter defending the (mildly surprising) conclusion that having a black-sounding name like DeShawn is not a disadvantage in the US, once you take account of the class, education and family backgrounds variables typically associated with such a name. Having named their own baby Freakonomics, economist Steven Levitt and journalist Stephen Dubner must be pretty confident on this point, and their high ranking on the New York Times bestseller list would appear to bear them out.
The subtitle of Freakonomics is A Rogue Economist Explores the Hidden Side of Everything and it’s a summary of some of the research contributions made by Leviit, the ‘rogue economist’ of the title. Indeed, the authors go to some pains to point out that Levitt is not really an economist at all, at least not in the ordinary sense of the term. He is quoted as telling his co-author, ‘I just don’t know very much about the field of economics. I’m not good at math. I don’t know a lot of econometrics, and I also don’t know how to do theory. If you ask me whether the stock market’s going to up or down, if you ask me whether the economy’s going to grow or shrink, if you ask me whether deflation’s good or bad, if you ask me about taxes – I mean, it would be total fakery if I said I knew anything about any of those things.’
It’s a surprising thing to say about someone who’s been awarded the John Bates Clark medal for the best American economist under the age of 40, but this does not appear to be mere false modesty. There’s very little economics in Freakonomics. Levitt is an incredibly smart social scientist, but most of his work could equally well have been done by someone trained in quantitative versions of sociology or poltical science.
Although he’s a product of Harvard and MIT and a full professor at Chicago, what Levitt has taken from the economics profession is not so much a body of theory to be applied, as a set of tools for empirical analysis and an unflinching willingness to look at social and policy issues without regard to social norms or received wisdom. More importantly, he’s combined all this with creative flair and an impressive capacity to see the right way of teasing compelling conclusions out of refractory data.
It’s not hard, for example, to see that, if teachers are rewarded and punished on the basis of scores from tests they administer themselves, that they’re likely to rig the results. What’s harder is to work out, as Levitt did, how cheating would manifest itself in answers to multiple choice tests, when the physical evidence that might have revealed cheating (the original test sheets) has long since been destroyed.
Similarly, it’s easy enough to see that real estate agents may be motivated to go for a quick sale and commission, rather than holding out for the best possible price, even though that would be in the interest of the sellers. but hard to see how you might test it. Levitt’s bright idea was to look at real estate agents selling their own homes. Sure enough, they waited longer and got more money. The disparity has diminished, but not disappeared, since the arrival of the Internet, and the sites like realestate.com.au that allow anyone with the time and judgement to become experts on the state of the market.
In terms of unflinching willingness to follow the data where it leads, there can be few examples more striking than the paper by Levitt and Donohue arguing that the drop in crime rates experienced in the United States in the 1990s was due, in large measure to the legalisation of abortion two decades earlier which, Levitt and Donohue hypothesised, prevented the birth of unwanted children at high risk of becoming criminals.
This is a striking and unsettling finding, at at time when the debate over abortion law in the United States is hotter than ever. Naturally there have been vigorous critiques. The most prominent of these, by John Lott and John Whitley seems like a very weak reed in view of the subsequent discrediting of Lott’s major work, on guns and crime. As Levitt and Dubner note in the gun crime context, Lott’s supporters were embarrassed by the revelation that he posted favorable reviews of his own work, and attacks on his rivals, using a female pseudonym, Mary Rosh. (Lott himself was unabashed, as were his employers at the American Enterprise Institute). Less embarrassingly, but more seriously, Lott apparently invented a survey which he used to support his claims, and engaged in other dubious maniplulations. Given the ease with which statistical analyses can be selectively biased, no reliance can be placed on Lott’s results. However, there is still every possibility that subsequent work using different data and methods may support the conclusions of Lott and Whitley or at least fail to replicate that of Levitt and Donohue.
What’s notable, though, is that the central premise of the proposed explanation, that unwanted children are more likely to end up as criminals, comes from the realm of sociology. Economists have had little to say on this subject.
One point where Levitt sticks with the verities of the economics tribe is his repeated insistence that incentives matter. The empirical studies certainly give plenty of support to the view that human beings are purposive agents, pursuing their own ends and responding creatively to their environment, which includes deliberately constructed incentives and market price signals. The alternative, structuralist idea, that people are passive bearers of socially constructed roles, gets short shrift.
Yet, in nearly every case considered by Levitt, incentives turn out not to work the way that might have been expected by the people who designed them. Parents respond to a small charge for late pickups from a childcare centre by increasing the number of late pickups; apparently, the specification of a monetary price cancelled any feeling of moral obligation.
In other cases, incentives are too weak to produce the expected results. Real estate agents are paid for selling houses at the highest possible price, but their share of any increase in the sale price (the commission percentage) is too small to induce them to ‘go the extra mile’ for their customers. Sumo wrestlers are rewarded for wins, but nonlinear jumps in the reward schedule lead to matches being fixed when they are vital for one contestant but marginal for the other.
Most striking of all is Levitt’s analysis of the economics of drug dealing in Chicago housing estates, based on data collected, at significant personal risk by sociologist Sudhir Venkatesh. We begin with the irony that harsher sentences, designed to deter gang activity brought small-time gang members into contact with Mexican and Colombian cocaine importers, and thereby helped to fuel the crack epidemic of the early 1990s.
Of even more interest is the fact, documented in detail using the account books of a drug gang, that the average street-level dealer makes about $3.30 an hour, less than the minimum wage. Levitt plausibly explaines this as a tournament, in which the dealers accept low pay and high risk in return for a shot at the position of gang leader, earning more than $100 000/year, tax free.
It’s easy to see, however, that in a gang with more than 50 members, the expected payoff is not that great Suppose that the leader can expect perhaps five years in the job before falling to the hazards of prison, a rival gang, or an ambitious subordinate, and that the average gang member would have to work at least that long on the street corner before aspiring to the top job. Then the expected return from the leadership lotter is no more than $2000/year or around $1 an hour, only a marginal supplement to the miserable returns documented by Levitt.
There are plenty of plausible explanations for this. For example, explaining the fact that the chance of winning lotteries is routinely overweighted has been one of the major tasks for theorists of generalised expected utility models, and the same explanations would be applicable here.
The fact remains though, that, while people respond to incentives, they don’t respond the way the people who created the incentives might expect, or the way textbook economics might predict. Not that long ago, most economists would have said this was a problem for psychologists or sociologists and left it at that. Increasingly, however, the barriers between economics and the other social sciences are breaking down, with the rise of subfields like behavioural economics and economic sociology.
If we are going to make progress in understanding how human beings actually behave, rather than how idealised social science models say they should behave, skill in extracting meaningul patterns from refractory data will be crucial. Steve Levitt is showing us the way.