Translation: why can't I google "examples of confounding variables" and publish the next installment in the "freako" series?
Chapter 1: Prostitution data is from Columbia's Sudhir Venkatesh.
Chapter 2: Data on life expectancy by month is from Douglas Almond and Bhashkar Mazumder, who also proposed the Ramadan solution; data on athletes' birthdates comes from Florida State Professor Anders Ericsson; terrorist education data borrowed from Alan Krueger; the ER data was compiled by Craig Feied.
Chapter 3: Altruism research was conducted by economist Gary Becker; results from the Ultimatum simulations were conducted by "a group of preeminent scholars" and published in Foundations of Human Sociality.
Chapter 4: The proposition of the seat belt came in the 1950s from Robert McNamara; modern crash data comes from the Fatality Analysis Reporting System (FARS) (Levitt only summarizes the newer data to update McNamara's previous finding).
Chapter 5: The data linking locally grown food consumption and greenhouse-gases comes from Carnegie Mellon Professors Weber and Matthew; the idea that carbon dioxide is not a greenhouse gas comes from Nathan Myhrvold, the former CTO of Microsoft.
I could go on for each chapter, but the point is clear.
It seems a lot of the original contribution from Levitt is just insufficiently justified assumptions, as mentioned by the bloggers before me. Levitt's value-add (if you can call it that) seems to be statements like "let's say it takes an average of one minute to remove and replace your shoes...so...the tax is the time equivalent of 14 lives per year." (93)
I know for a fact Subin, Jen, Sarah and I can walk you through those sort of assumption-to-conclusion progressions -- we practiced consulting case interviews for hours on end over the summer.
It just seemed odd to me that such a popular book would only require skills most undergraduates already have. Paper A says X, but Paper B says X is wrong because of Y. Too large a portion of Super Freakonomics followed this format, a point with which the New Republic concurs.
To be fair, Levitt brilliantly synthesizes and presents the data cited in his book - and he certainly makes appropriate citations. However, I am a little peeved that Levitt gets another best-seller and furthers his reputation for cutting-edge economics while largely piggybacking off the research of others.
And yes, by 'peeved' I do mean 'jealous'.
In addition, I was very intrigued by the argument about birthdays and professional athletes. It seems that Levitt's thesis still holds up: 2005 data provided by Major League baseball revealed that October birthdays were far and away the most common. Sure enough, the Little League birthday cutoff is still July 31. A study on NHL hockey players revealed a similar trend for January births, since most international cutoffs are December 31.
The performance of these athletes once they reach the professional level, interestingly, has very little correlation with birth month.