Marc F. Bellemare Posts

From an article in the Des Moines Register:

World food prices in March fell to their lowest level since 2010 as consumers benefited from a glut of commodities, the United Nations said this week.

The UN Food and Agriculture Organization (FAO) said the food price index, which measures monthly price changes in cereal, dairy, meat, sugar and oilseeds, fell 2.6 points in March to 173.8 points–the lowest since June 2010.

“Overall, except for a pause in October 2014, the index has been falling steadily since April 2014, on account of large global supplies for most commodities included in the index,” the UN said in its monthly report.

There is more here on the FAO’s  webpage dedicated to the food price index:

The FAO Food Price Index averaged 173.8 points in March 2015, down 2.6 points (1.5 percent) from its revised February value and nearly 40 points (18.7 percent) below its level in March 2014. Sugar prices dipped particularly strongly in March, with more modest declines recorded by vegetable oils, cereals and meat. By contrast, dairy values rose for the second consecutive month, departing from the general negative trend that dominated the other commodity markets. Overall, except for a pause in October 2014, the Index has been falling steadily since April 2014, on account of large global supplies for most commodities included in the Index.

As far as I’m concerned, this is good news, because it means more of the world’s poor people are able to afford food, and it also means that the world should see fewer instances of social unrest and food riots.

When I started working on food prices four years ago, everyone was worried about high food prices. Never mind the fact that low food prices are good for consumers and that every single individual in the world is a consumer of food, I give it about six months before the media turns this nonstory into a story and goes nuts about low prices, and how those are a harbinger of doom because some the most inefficient food producers might go out of business.

ht: Janet.

This guest post by Jennifer N. Brass, assistant professor in the School of Public & Environmental Affairs at Indiana University, discusses the Trading Game, an experiment I run the first day of class in my intermediate microeconomics class. My original post on the Trading Game was my most popular post ever. You can follow Jen on Twitter at @jennifer_brass.

I teach an introductory undergraduate public policy course called “National & International Policy.” The goal of the course is to introduce students to the policy process, the range of actors involved in decision-making and implementation, and the political and economic factors that go into how policies are made and implemented. To highlight the tensions between the national policies of individual countries and international agreements, I focus one section of the course on international trade policy.

At the beginning of this unit of the course, we play the Trading Game. Like Marc, I go to the local dollar store and buy a wide range of trinkets, from toy cars to sidewalk chalk to Tupperware, sponges and bar soap. Some items are playful, others are useful. Some are neither.

Last week the Midwest Economics Association (MEA) meetings were taking place in Minneapolis. Because a few friends were presenting at MEA, I decided to go check out the sessions at which they were presenting.

At one of the sessions I attended, a graduate student presented a very cool paper in which he had run a randomized controlled trial to determine the effect of a treatment variable D on an outcome Y, randomizing D and collecting information on a number of control variables X in addition to collecting information on Y.

The graduate student came from a good department, so he carefully motivated his paper by talking about the policy relevance of the relationship between D and Y, explaining that policy makers cared deeply about said relationship, and how they made a big deal of it.

When presenting his results, the presenter did what we commonly do in economics, which is to show a table presenting several specifications of the regression of interest, from the most parsimonious (i.e., a simple regression of Y on just D) to the least parsimonious (i.e., a complex regression of Y on D and all the available controls X).

The problem, however, was that the R-squared measure–the regression’s coefficient of determination–for the simple regression of Y on just D (i.e., the most parsimonious specification) was about 0.01, meaning that the treatment variable D explained about 1 percent of the outcome of interest.