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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.

I began a three-year term as associate editor over at Food Policy at the end of 2013, which means that I handle submissions in my areas of expertise, deciding which manuscripts get reviewed and which ones get desk rejected, selecting reviewers for those manuscripts that do get reviewed, and so on.

Once again, I wanted to feature a few articles from the latest issue of the journal. There is nothing special about those articles beyond the fact that I thought they would be of interest to readers of this blog.