1. Do customs and postal service discriminate against “atheist” parcels?
2. Now there are wristbands to monitor whether doctors are washing their hands. (HT: R.E. Riker)
3. Dan Ariely is offering a free online course: “A Beginner’s Guide to Irrational Behavior.” Sign up here.
4. Dan Pallotta argues that non-profits should be run like real companies.
5. A new study of English literature finds that the use of mood words is steadily decreasing.
The weather — its effects on the environment, behavior, sports, and society — has long been of interest to Freakonomics. Now a new working paper from Warren Anderson, Noel D. Johnson, and Mark Koyama explores the effects of cold growing seasons on discrimination against Jewish communities between 1100 and 1800:
What factors caused the persecution of minorities in medieval and early modern Europe? We build a model that predicts that minority communities were more likely to be expropriated in the wake of negative income shocks. We then use panel data consisting of 785 city-level expulsions of Jews from 933 European cities between 1100 and 1800 to test the implications of the model. We use the variation in city-level temperature to test whether expulsions were associated with colder growing seasons. We find that a one standard deviation decrease in average growing season temperature in the fifteenth and sixteenth centuries was associated with a one to two percentage point increase in the likelihood that a Jewish community would be expelled. Drawing on our model and on additional historical evidence we argue that the rise of state capacity was one reason why this relationship between negative income shocks and expulsions weakened after 1600.
This is a transcript of the Freakonomics Radio podcast “100 Ways to Fight Obesity.” [MUSIC: Jonathan Clay; “Carousel” (from Everything She Wants)] DUBNER: Steve Levitt is my Freakonomics friend and coauthor. He stands about 5-foot-11 and weighs 160 pounds. So he does not have a weight problem. But he has been thinking about our collective […] Read More »
This is a transcript of the Freakonomics Radio podcast “How Money Is March Madness?” Kai RYSSDAL: Time now for a little Freakonomics Radio. It’s that moment in the broadcast every couple of weeks we talk to Stephen Dubner, the co-author of the books and the blog of the same name. It is, yes, yes, it is […] Read More »
Economic analysis is itself value-free, but in practice it encourages a cosmopolitan interest in natural equality. Many economic models, of course, assume that all individuals are motivated by rational self-interest or some variant thereof; even the so-called behavioral theories tweak only the fringes of a basically common, rational understanding of people. The crucial implication is this: If you treat all individuals as fundamentally the same in your theoretical constructs, it would be odd to insist that the law should suddenly start treating them differently.
Cowen concludes by exploring a modern-day application of this putatively egalitarian core:
A distressingly large portion of the debate in many countries analyzes the effects of higher immigration on domestic citizens alone and seeks to restrict immigration to protect a national culture or existing economic interests. The obvious but too-often-underemphasized reality is that immigration is a significant gain for most people who move to a new country.
A working paper (abstract; PDF) from economists Michael Baker and Kevin Milligan advances another possible explanation for the lagging academic performance of boys — preschool boys, at least. Here’s the abstract:
We study differences in the time parents spend with boys and girls at preschool ages in Canada, the UK and the US. We refine previous evidence that fathers commit more time to boys, showing this greater commitment emerges with age and is not present for very young children. We next examine differences in specific parental teaching activities such as reading and the use of number and letters. We find the parents commit more of this time to girls, starting at ages as young as 9 months. We explore possible explanations of this greater commitment to girls including explicit parental preference and boy-girl differences in costs of these time inputs. Finally, we offer evidence that these differences in time inputs are important: in each country the boy-girl difference in inputs can account for a non-trivial proportion of the boy-girl difference in preschool reading and math scores.
The authors’ results also indicate that the time differences are not due to parents’ gender preferences, but may be related to the opportunity cost of the mother’s time. ”Given that time spent reading with children (primarily boys) increases after the introduction of a new child care subsidy, the parental time inputs we study may not be easily substituted by non-parental care,” they write. “Instead, this finding is consistent with a story in which boys are less rewarding to teach, and parents are more willing to persevere with boys once they are not responsible for their care throughout the day.”
If you’re the kind of person who cares about “The Folly of Prediction” and The Signal and the Noise, you may want to read Amy Zegart‘s Foreign Policy piece about predictions. Making predictions within the intelligence community, for example, is a different game than betting on basketball:
In March Madness, everyone has access to the same information, at least theoretically. Expertise depends mostly on how geeky you choose to be, and how much time you spend watching ESPN and digging up past stats. In intelligence, however, information is tightly compartmented by classification restrictions, leaving analysts with different pieces of data and serious barriers to sharing it. Imagine scattering NCAA bracket information across 1,000 people, many of whom do not know each other, some of whom have no idea what a bracket is or the value of the information they possess. They’re all told if they share anything with the wrong person, they could be disciplined, fired, even prosecuted. But somehow they have to collectively pick the winner to succeed.
In other spheres, however, predictions just keep getting better. “Smart people are finding clever new ways of generating better data, identifying and unpacking biases, and sharing information unimaginable 20 or even 10 years ago,” writes Zegart.