My close friend, colleague, and frequent co-author John List has written a popular (non-academic) book with another economist, Uri Gneezy. John and Uri are pioneers in the area of “field experiments” which bring the power of randomized experiments into real-world settings. In my opinion, field experiments are the future of empirical economics. We’ve written at length in our books and on our blog about the amazing work these two have been doing. I’ve had the chance to read John and Uri’s book, and I loved it.
The thing they can’t figure out, however, is what to call the book! If only my sister Linda – the greatest namer of things the world has ever known — were still around, she would figure out a great title for sure. In her absence, they’ve asked if I could mobilize the collective genius of you, the Freakonomics blog readers.
Okay, so here is the deal. Below, I’ve provided some information on the book and links to some materials that might prove useful to you in coming up with a name. You have two days to generate great titles for the book, which you can submit as comments on this blog post. Read More »
I made a mess out of this year’s Kentucky Derby. The worst part is that a bunch of friends placed bets using my picks, collectively losing a large stack of money.
After the Kentucky Derby, I blogged about the misery, noting what a strange race the Derby was:
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The race is 1.25 miles long and there were 19 horses in the race. Of the eight horses who were in the front of the pack after one-fourth of a mile, seven ended up finishing in back: 12th, 14th, 15th, 16th, 17th, 18th, 19th. Only one horse that trailed early also finished poorly, and that horse started terribly and was way behind the field from the beginning. In contrast, the horses who ended up doing well were in 16th, 15th, 17th, 12th, and 18th place early on in the race. Basically, there was a nearly perfect negative correlation between the order of the horses early in the race and the order of the horses at the end of the race!
My condolences to anyone who bet my picks in the Kentucky Derby. Of the four horses I liked, the best finisher was Revolutionary in third place, but even that was unimpressive because he surprised me by going off as the second favorite in the betting. Just be glad I didn’t post my picks for the entire day’s racing at Churchill Downs…the few friends I did give those picks to are cursing me today!
The Kentucky Derby was extremely interesting, however, from a statistical perspective. Here is a link to the results chart for the race. If you don’t study horse racing, it will just look like gibberish. If you know how to read a results chart, you will see a remarkable pattern jump out of the numbers. The race is 1.25 miles long and there were 19 horses in the race. Of the eight horses who were in the front of the pack after one-fourth of a mile, seven ended up finishing in back: 12th, 14th, 15th, 16th, 17th, 18th, 19th. Only one horse that trailed early also finished poorly, and that horse started terribly and was way behind the field from the beginning. In contrast, the horses who ended up doing well were in 16th, 15th, 17th, 12th, and 18th place early on in the race. Basically, there was a nearly perfect negative correlation between the order of the horses early in the race and the order of the horses at the end of the race! Read More »
Every year I post my picks for the Kentucky Derby. Last year I actually did well, for a change. In a twenty-horse field, I picked three horses to do well, and two of them ended finishing first and second. The winner was 15-1. I also made a correct prediction as to which horse would finish last. I got that one right as well.
So here we go again…
Let me start by saying that the crystal ball (actually the computer algorithm) is a little fuzzy this year. There are four horses that all look equally good to me: Falling Sky, Java’s War, Itsmyluckyday, and Revolutionary. All will be longshots, I suspect, with odds between 15-1 and 25-1.
The model also kind of likes Verrazano, who might be the favorite in the race. If I were betting, I might include him in my exotic bets. Read More »
Have you ever noticed that whenever you rent a car, when they give you the keys to the vehicle, there are always two sets of keys? But the two sets of keys are attached to the same key chain, and no matter how hard I’ve tried, I have never figured out a way to detach one set of keys from the other.
What could possibly be the point of giving customers two sets of keys that can’t be separated? The downside is that if the keys get lost, two sets of keys are gone. Also, the keys are much bulkier in my pocket than otherwise would be the case.
The only possible explanation I can see is that since no one carries around two attached sets of keys to the vehicle they own, people are less likely to confuse their own car keys with those of the rental vehicle. It just doesn’t seem like that could be the logic, however.
So can anyone explain to me the real reason rental car companies do this?
I have spent the last 20+ years of my life doing academic research and popular writing on economics. I’ve been lucky, and my work has gotten a lot of exposure. I certainly have had a lot of fun along the way.
But, I think I can honestly say that no government has ever changed a law or a public policy as a result of my work. Sometimes politicians cite my research in pushing an agenda but having talked to these politicians, it is clear they had the agenda first, and then they went looking for research – any research – that would support their position. When I’ve taken unpopular stances (like saying children’s car seats don’t work well), there has never been even a sliver of political movement on the issue.
Finally, however, I think I may be on the verge of my first policy victory. Read More »
Three of my colleagues and friends at the University of Chicago — Kerwin Charles, Erik Hurst, and Matt Notowidigdo — recently presented some new research that aims to understand the ups and downs in the U.S. labor market. It’s more serious and important than the usual stuff we deal with on the blog, but every once in a while we deviate from trivialities when something really good comes along.
They’ve been kind enough to put together a layperson’s version of the research below. For those looking for the full-blown academic version, you can find that here.
A Structural Explanation for the Weak Labor Market
By Kerwin Charles, Erik Hurst, and Matt Notowidigdo
In the aftermath of the Great Recession, the labor market has remained anemic. Between 2007 and 2010, the employment-to-population ratio of men between the ages of 21 and 55 with less than a four-year degree fell from 82.8 percent to 73.8 percent. As of mid-2012, the employment-to-population ratio for these men remained depressed at 75.6 percent.
In our new working paper (abstract; full PDF), we show that the recent sluggish labor market in the U.S. – particularly for prime age workers without a college degree – can be traced back to the large sectoral decline in manufacturing employment that occurred during the 2000s. After decades of relative stability, total manufacturing employment in the U.S. fell by 3.5 million jobs between the beginning of 2000 and the end of 2007 (see chart below). These manufacturing jobs were lost even before the Great Recession started. During the recent recession, another 2 million manufacturing jobs were lost. While there is talk of a recent manufacturing rebound in the U.S., the recent increase is only a tiny fraction of the total manufacturing jobs lost during the 2000s. Read More »
Freakonomics Experiments has succeeded beyond our wildest dreams.
We’ve already had more than 15,000 coin tosses.
The single most popular question: “Should I quit my job?”
There also seem to be a whole lot of troubled relationships out there. But not many people agonizing over whether to grow a beard.
Here’s Tim Harford’s take on the project in a cleverly written Financial Times piece.