‘Metrics Monday: Good Things Come to Those Who Weight–Part I

I was sitting in my office on Friday afternoon when one of our third-year PhD students dropped by with an applied econometric question: “When should I use weights?”

After telling her to go read Solon et al.’s 2015 piece in the JHR symposium on empirical methods, I decided to reread that paper for myself and blog about it this week. In the near future, in part II, I’m hoping to tackle Andrews and Oster’s new NBER working paper on weighting for external validity.

Before I begin, some clarification: throughout this post, I’ll be discussing the use of sampling weights. If you are a Stata user, this refers to that statistical package’s -pweight-, i.e., “weights that denote the inverse of the probability that the observation is included because of the sampling design.” I have never had to rely on -aweight-, -fweight-, or -iweight-, so I wouldn’t know when to use them.

Suppose you oversample a specific group in order to get more precise estimates for that group. For instance, suppose you are interested in the opinion of LGBTQ students. If you randomly sample individuals from a given population of students, you may not have enough LGBTQ respondents in your sample, and so whatever descriptive statistics you come up with for that sub-group might be too noisy. Thus, you may wish to over-sample LGBTQ respondents in order to improve precision. What I mean by this is that you would randomly sample respondents from each group–LGBTQ and non-LGBTQ–until you have the right number. So if you target a sample size of n=100 and you’d like 50% respondents from each group, you split the population in two groups (assuming that’s easy to do; in the case of LGBTQ students, it might not be easy to do) and sample from each until each group has 50 observations. Continue reading


For better or for worse, I seem to have acquired a reputation as the go-to guy regarding the price of trendy foods. So a few weeks ago, I talked to Bloomberg reporter Kyle Stock about the price of avocados. Here is the article that he wrote, which also features insights from my Purdue colleague David Widmar.

This is what I had to say:

When a new tree is planted, it won’t bear much fruit until its third year. At the moment, that stings consumers. Supply can’t catch up with demand in a manner of months, as it can with a product such as the tomato. All the while, though, farmers are watching the price, ready to plant where they can and cash in. This fall’s expensive avocados may trigger a glut in two or three years. That’s what happened with quinoa in 2015, according to Bellemare. After doubling to record highs, prices for the trendy grain swooned.

“When you see this kind of crazy demand, there are a lot of people sitting on the margins that decide to get in and plant,” he said. “Eventually, all those extra-normal profits get competed away.”

In the process of preparing for my call with Kyle, I read a whole bunch about avocados, as they were a commodity I knew little about. Continue reading

Econ PhDs and the Agricultural and Applied Economics Job Market

A friend who is finishing his PhD in economics writes:

If you have time, would you mind sharing your thoughts on working in [an agricultural and applied economics] department as well as any tips you may have for customizing job applications for ag/resources places?

This is a good question, and I am grateful for the blog fodder. After sitting on two search committees in our department, I noticed that econ PhDs often didn’t do well in their interviews with us because they hadn’t taken the time to study the differences between economics departments and agricultural and applied economics departments.

As with many questions job-market related, John Cawley’s guide to the job market is the best overall resource and it should be the first place you look. But here are some thoughts of my own, idiosyncratic and in no particular order: Continue reading