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Last month, Ben Chapman and Don Schaffner, who host the Food Safety Talk podcast, discussed my January Gray Matter column in the New York Times in January, in which I discussed my work on farmers markets and food-borne illness.

Their discussion was even-handed, and Don (I think it was him; I listened to the segment only once, over a month ago) demonstrated a surprising understanding of the working paper culture in economics, wherein we circulate working papers well ahead of submitting for publication so as to make our work better in view of publishing it in better journals. But the one part which made my ears perk up was when Ben asked Don (or the other way around; again, it’s been a while since I listened) why my coauthors and I had looked at the relationship between farmers markets and all those seemingly irrelevant illnesses, and Don said (and I’m paraphrasing), “I don’t know, it looks like data mining.”

Chose promise, chose due. In the last installment of ‘Metrics Monday, I mentioned that my coauthors Johanna Fajardo-Gonzalez and Seth Gitter and I had just recently put the finishing to our paper on the welfare impacts of rising quinoa prices. At long last, after more than three years of thinking about this issue, here is the abstract:

Riding on a wave of interest in “superfoods” in rich countries, quinoa went in less than a decade from being largely unknown outside of South America to being an upper-class staple in the United States. As a consequence of that rapid rise in the popularity of quinoa, the price of quinoa tripled between 2006 and 2013. We study the impacts of rising quinoa prices on the welfare of Peruvian households. Using 10 years of a large-scale, nationally representative household survey, we combine pseudo-panel and difference-in-differences methods to look at the relationship between (i) the purchase price of quinoa and the value of household consumption, which we use here as a proxy for household welfare, and (ii) household quinoa production and household welfare. We find that increases in the purchase price of quinoa are associated with a significant increase in the welfare of the average household in areas where quinoa is consumed, which suggests that the quinoa price increase has had general equilibrium effects extending to non-producers. We also find that quinoa production is associated with a faster rate of growth of household welfare, but only at the height of the quinoa price boom. Our findings are robust to a number of different specifications.

And here is a link to the paper itself. But because an image is worth a thousand words, here is a striking image from the paper:

Back from spring break which, even though I am on leave this semester, I used to take a break from blogging and travel to (i) Peru, to assess the feasibility of field experiments I am planning on conducting there and (ii) Ithaca, NY, to present my work on farmers markets and food-borne illness at the Dyson School of Applied Economics and Management in the future Cornell College of Business.

For today’s installment of ‘Metrics Monday, suppose you have data that consists of repeated cross sections. To take an example from my own work, suppose you have 10 years worth of a nationally representative household survey, but the data are not longitudinal. That is, for each year, whoever was in charge of collecting the data collected them on a brand new sample of households.

Obviously, because the data are not longitudinal, the usual panel data tricks (e.g., household fixed effects) are not available. So what can you do if you want to get closer to credible identification?