Leo Tolstoy

Leo Tolstoy begins Anna Karenina with the famous lines: “All happy families are happy in the same way. All miserable families are miserable in their own way.”

I did my graduate thesis on the spatial relationship of stochastic variables. The stochastic variable which I found most interesting at the time was farmer corn yields. Since yields can’t go any lower than zero I had a preconceived notion that in bad years all farmer yields were bunched up down close to zero and on good years yields were very spread out with some farmers having average yields and some having nearly double their average yields.  I was pretty surprised when I found that good year yield distributions look like this:

IL Corn 2006

and bad years look like this:

IL Corn 2002

I was surprised by the notion that when yields are relatively low, like in 2002, the distribution has so much more dispersion. And in “good” years, like 2006, the distribution is rather compact. This pattern holds across other crops, other states, and different levels of aggregation/disaggregation.

So in happy years all farmers are happy together, but in miserable years each one in miserable in their own special way.

There are interesting spatial correlation impacts of this which are quite intuitive. If a good year is defined as ‘everyone does well’ then the spatial correlation is quite high since everyone is doing well. But bad years are defined by some doing ok, and some doing quite bad. This results in much lower spatial correlation.

Sooooo… BOTH the distribution shape AND the correlation matrix of crop yields must vary with yield level. That makes a proper stochastic model a bit tricky, no? Typically we assume the correlation matrix to be fixed and not a variable. That’s why it’s never boring to be a stochastic agricultural economist.

Footnote: The above graphs are PDFs based on county yields, not producer yields, but yields are fractal so the producer level yield distributions have a very similar shape.  Data is from the National Agricultural Statistical Service (NASS).  PDFs were done in R using the density function.

Hal Varian - Superstar

So one of the top jobs in the world has to be the friggin’ gig that Hal Varian has. He’s the chief economist at Google. He might as well have business cards that read “Hal Varian, Mother Friggin Rock Star.” Plus he did his BS at MIT and got his MA in math and his PhD in econ at Berkeley. According to Wikipedia (aka digitalgod) he finished his education at MIT in ‘69 and his PhD at Berkeley in ‘73. WTF? It was the early 70’s and he was in Berkeley! Some of his classmates are STILL there trying to figure out which end of the bong their dissertation is supposed to fall out of. So to get the masters, the doctorate, and smoke the chronic in 4 years is amazing. And it just contributes to his legend. Is it just me or does he look like a slightly more healthy Steve Jobs in that picture to the left?

You may not know that he wrote a textbook on Intermediate Microeconomics. It’s only $55 on eBay! Since the he wrote the book on microecon, not macro, I take back all that reefer smoking smack I was saying. Becasue it’s a well known fact among economists that only macroeconomists smoke pot. They also practice voodoo, but that’s fodder for another post.

That’s all well and good, but why am I writing this? Glad you ask. Let me just cut to the chase:

Hal Varian Thinks I Am Sexy!

Yep, he said so in an interview with McKinsey Quarterly Magazine. But don’t take it from me, read for yourself:

I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

Yes, I know that I am an economist, not a statistician, but Hal’s trying to be discrete. It was just a little too obvious he was talking about me when he got to the part about “visualize the data, communicate the data, and utilize it effectively.” As much as I am flattered, I have to be perfectly clear here. I am happily married and wouldn’t give that up for the world. But if you ever want to have me over to the Googleplex for free lattes and sushi, Hal, you know my number.

-JD