There has been a lot of blogospheric discussion of the the evidence that has been presented by Catherine Rampell at the New York Times Economix blog showing that countries in which people spend less time per day eating and drinking tend to be places with more obesity. This has been headlined as “fast food equals fat food.”
The author’s take:
Coincidence? Maybe, maybe not. ….
There does seem to be some correlation (although, as we all know, correlation is not causation). And note, of course, where America lies on this chart.
In her prior post on the topic, she had this to say:
Interestingly, of the 18 O.E.C.D. countries have current time-use surveys, the French spend the most time eating and drinking, and yet the French have the fifth lowest obesity rate (sixth lowest among all 30 O.E.C.D. countries, as many countries have data on obesity rates but not time use). Perhaps fast food really is the problem.
Like many people, I questioned whether the scatterplot presented in the post really provided much evidence of causality, so I went back to the original data tables to take a look at it (because I’m so cool).
I recreated the original analysis (minus the inclusion of the OECD average as a data point in the regression, for what I assume are obvious reasons). I get pretty much the same picture, and using a log regression form, get what looks to be the same trend line. The R-Squared on the regression (not noted in the original post, as far as I could see) is 26%. Without the U.S. and Mexico, it goes to about 6%, and becomes statistically insignificant.
But what was really interesting is that there are five other time categorizations provided at the source website. Here’s the same data plot, but using “Time Spent Doing Unpaid Work” instead of “Time Spent Eating and Drinking”:
Huh. This relationship, produced from the same data source, is about twice as strong (R-Squared = 52%) as the one that was reported. It took me literally five minutes of work to discover it. Why do you think that one was reported but not the other? This appears to be a textbook example of the human tendency to accept correlations as “not definitive, but part of the overall picture of evidence for causality” when such data serves to confirm pre-existing beliefs, and to ignore it otherwise.