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I spent part of last week in Clermont-Ferrand, at the Fondation pour les études et recherches sur le développement international (FERDI), at a workshop with the lengthy title of “Commodity Market Instability and Asymmetries in Developing Countries: Development Impacts and Policies,”  and which was convened by Alexandros Sarris, of the University of Athens and FERDI.

The workshop’s purpose and themes were as follows:

The general purpose of the workshop is on the one hand to examine the state of the art in the area of asymmetries and irreversibilities relating to commodity market instability and development, with the purpose to first pinpoint gaps in current research, and secondly to highlight promising areas of policy intervention to aid developing countries to manage/cope with market instability. As the topic is very large, and impossible to cover in all of its various aspects, the workshop will restrict its proceedings to a set of specific themes that are judged to have been underemphasized in previous empirical and policy development economics research. While commodity market instability can originate in many ways, the workshop will be restricted to market instability arising from natural or other unpredicted events, as well as unforeseen market developments.

You can find the program and the papers presented at the workshop here by clicking on the relevant tab. The event was an excellent occasion meet with colleagues old and new and discuss a topic which has occupied a sizable proportion of my professional life as well as to receive feedback on the experimental work I have been doing with Yu Na Lee and David Just on price risk preferences.

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It often happens in the course of doing empirical work that we wish study the relationship between some variable of interest D and some outcome Y, but that we don’t have access to a good measure of D. Rather, what we have instead is a proxy for D, which Wikipedia defines as

a variable that is not in itself directly relevant, but that serves in place of an unobservable or immeasurable variable. In order for a variable to be a good proxy, it must have a close correlation, not necessarily linear or positive, with the variable of interest.

For example, we may observe a dummy variable for whether one has started a business as a proxy for entrepreneurial ability. Or we may observe one’s IQ as a proxy for intellectual ability. Or we may observe the frequency of elections as a proxy for democracy. The possibilities here are endless.

For the sake of argument, then let’s denote our proxy variable–what we actually observe in lieu of D–as D*, so that