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Marc F. Bellemare Posts

Published January 5, 2012

Coordination Failure, Self-Fulfilling Prophecies, and Rising Food Prices

Fiorenzo Conte, in a post on the London School of Economics’ Development Studies Institute’s student blog, makes a crucially underappreciated point when it comes to food prices:

The price spike was ignited by a series of decisions which made a lot of sense from the perspective of every individual actors who took them. Each of this “rational” choice was dictated by the goal of achieving the food security in each country in face of a growing fear that the world was running out of rice. More precisely the fear that there might have been a shortage because the shortage never materialized in reality. Rational choices compounded by fear determined the very irrational outcome of a price spike.

The first culprit was the government of India which made “food for all” its flagship. (…) To do so it banned the export of rice out of the country. (…)

The next thing was that rice prices soared by 20% overnight. Governments all over Asia rushed to buy as much rice they could and hoard it in the expectation of a future scarcity of rice signaled by the price jump. (…) What government officials in the Philippines did was to tell their people to eat less rice so that the government could have bought less rice (and this was probably the least reasonable of the reactions). In response to this Filipinos rushed to buy as much rice as they could because they understandably interpreted the message from the government as “we are running out of rice.”

In a recent American Journal of Agricultural Economics article (ungated version here), Will Martin and Kym Anderson estimate that almost half of the increase in rice prices between 2006 and 2008 was due to country-level policies such as the ones described above.

This is a classic case of coordination failure (and, in the case of the Philippines, of a self-fulfilling prophecy). Unfortunately, global policy makers have very little say as to what goes on within countries. Even if they did, international organizations react way too slowly to be effective during food crises — this is especially true of the United Nations (the “colossus with feet of clay” analogy is particularly apt here), a little less so of the World Bank and the IMF.

Published January 5, 2012

FML, Food Prices Edition

Graziano said he expected that food prices wouldn’t rise much but that they also wouldn’t fall. “But volatility will remain, that is clear,” he said.

That’s Jose Graziano da Silva, talking to the CBC. Mr. Graziano is the new head of Food and Agriculture Organization (FAO) of the United Nations (UN).

Mr. Graziano seems to imply that food price volatility is the problem. But we know that it is rising food prices, and not unexpected upward or downward movements — the definition of volatility — in food prices that actually harm the poor. Food price volatility harms food producers and those who are net sellers of food, but it is rising food prices that hurt net food consumers. Let’s not forget that the overwhelming majority of the world’s poor are net food consumers.

I have explained here, here, here, here, and there that rising food prices — not food price volatility — harm the world’s poorest. And that’s just for the light reading — there’s a whole research back end to my claims. Not that it appears to be of interest anyone at FAO, though.

But really, the subtle distinction between the welfare impacts of rising food prices and food price volatility is the least of Mr. Graziano’s problems. Indeed, going back to the quote above, if prices neither go up or down, how can they remain volatile? FML.

(HT: Kim Yi Dionne, via Twitter.)

Published January 4, 2012

Linear Regression and Causality for Neophytes

(This is an update on a post I had initially written at the start of the academic year. I figured it would come in handy, as many of us are busy writing our syllabi for the spring semester.)

If you teach in a policy school, a political science department, or in an economics department that grants Bachelor of Arts instead of Bachelor of Science degrees, chances are some of your students are not quite conversant in the quantitative methods used in the social sciences.

Many of the students who sign up for my fall seminar on the Microeconomics of International Development Policy or for my spring seminar on Law, Economics and Organization, for example, are incredibly bright, but they are not familiar with regression analysis, and so they don’t know how to read a regression table.

This makes it difficult to assign empirical papers in World Development for in-class discussion, let alone empirical papers in the Journal of Development Economics.

While I do not have the time to teach basic econometrics to students in those seminars, I have prepared two handouts for them to read in preparation for reading papers containing empirical results, which I thought I should make available to anyone who would rather not spend precious class time teaching the basics of quantitative methods. I have used both these handouts in my development seminar last fall, and my students said that they had learned quite a bit from reading them.

The first handout is a primer on linear regression, which shows analytically and graphically (and hopefully painlessly) what a regression does, and why it is such a useful tool in the social sciences. Perhaps more importantly, this handout also explains how to read a regression table.

The second handout primer on the identification of causal relationships in the social sciences, which discusses the distinction between correlation and causation and explains two ways in which social scientists go about making causal statements (i.e., randomized controlled trials and instrumental variables), with a few examples. I suggest supplementing this handout with a reading of Jim Manzi’s “What Social Science Does–and Doesn’t–Know” in City Journal as well as with Esther Duflo’s TED talk.

Of course, neither handout is a substitute for a course in econometrics or on research design, respectively, but these handouts are intended primarily for undergraduates or Masters students with little to no quantitative background.

(Update: This post by Tom Pepinsky also offers a very good introduction to the identification of causal relationships. HT to Chris Blattman for this great find.)