The Next Great Famine?

It is rare that the New Yorker talks about food security, but last week, the magazine had an interesting article by Amy Davidson on climate change and food security, which it illustrated by discussing the (almost-forgotten) Great Famine of 1315-1317:

One of the most important insights of recent studies is that, when the climate changes, it can do so swiftly and relentlessly. It is possible, in a human lifetime, to see sea levels rise and ice shelves break away, and, when they do, nothing about what happens next can be taken for granted. The climate record is full of sudden disasters. … The Great Famine looks like a fourteenth-century example of what we now call extreme weather. … We have built cities and economies on assumptions about the seasons that may prove unstable. The best models we have now project that, as a consequence of climate change, the frequency of extreme-weather events, from superstorms to droughts, will increase sharply.

A particularly alarming prospect is the sustained failure of the South Asian monsoon. The food supply for more than a billion people relies on the rains of the monsoon season. Models suggest that, in the next century, monsoons will become more and more erratic and extreme. A failed monsoon can mean that the rain hasn’t come, or that it has come in the wrong place for the wrong amount of time. In recent years, India has experienced droughts but also floods, like the one that wreaked havoc in Chennai in December.

Given that the New Yorker article alludes to the possibility of a great famine, it might be wise to remember Amartya Sen’s words in a 1980 World Development article: Continue reading

My Gray Matter Column in the New York Times

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Last summer, I blogged about some work that I had been doing in which my coauthors and I looked at the statistical relationship between farmers markets and food-borne illness.

A few weeks later, I received an email from an editor at the New York Times asking whether I would be interested in submitting an op-ed to the Gray Lady on that same topic. At the time, I was about to embark on several weeks of work-related travel; then, the fall semester hit, with its groundswell of teaching; finally, my household’s dependency ratio went up just before Thanksgiving, all of which left me with little to no time to work on farmers markets.

But recent events about food-borne illness (specifically, the seemingly Chipotle-related E. coli outbreaks) and no teaching this semester (due to being on leave at the U’s Institute for Advanced Study) made me think to get back in touch with the Times editor who asked me for the piece in the first place.

After a good amount of back and forth with folks at the NYT–a process through which I learned quite a bit–what follows (and the picture above) are the first few paragraphs of what came out of it; you can read the whole thing on the New York Times website here. The piece ran in print in the Sunday Review of the Times on January 17. Continue reading

‘Metrics Monday: The Tobit Temptation

And because thou wast acceptable to God, it was necessary that temptation should prove thee. And now the Lord hath sent me to heal thee … — Tobit 12:13.

This week I wanted to discuss tobit estimators. In case you are not familiar with it, Wiki describes the tobit estimators (people say tobit “models,” but I don’t like calling estimators models, which confuses theory with empirics a bit too much for my taste) as

a statistical model proposed by James Tobin (1958) to describe the relationship between a non-negative dependent variable Y and an independent variable X. The term tobit was derived from Tobin’s name by truncating and adding -it by analogy with the probit model.

The model supposes that there is a latent (i.e. unobservable) variable Y*. This variable linearly depends on X via a parameter (vector) b which determines the relationship between the independent variable (or vector) X and the latent variable Y* (just as in a linear model). In addition, there is a normally distributed error term U to capture random influences on this relationship. The observable variable Y is defined to be equal to the latent variable whenever the latent variable is above zero and zero otherwise.

There are many types of tobits–Wiki lists five, which are such that Continue reading