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Marc F. Bellemare Posts

Published March 20, 2012

Think Genes Predict Social and Political Behavior? Not So Fast

My colleague Evan Charney has a very nice article in the most recent issue of the American Political Science Review:

Political scientists are making increasing use of the methodologies of behavior genetics in an attempt to uncover whether or not political behavior is heritable, as well as the specific genotypes that might act as predisposing factors for—or predictors of—political “phenotypes.” Noteworthy among the latter are a series of candidate gene association studies in which researchers claim to have discovered one or two common genetic variants that predict such behaviors as voting and political orientation. We critically examine the candidate gene association study methodology by considering, as a representative example, the recent study by Fowler and Dawes according to which “two genes predict voter turnout.” In addition to demonstrating, on the basis of the data set employed by Fowler and Dawes, that two genes do not predict voter turnout, we consider a number of difficulties, both methodological and genetic, that beset the use of gene association studies, both candidate and genome-wide, in the social and behavioral sciences.

The emphasis is mine. Having seen Evan give a fascinating presentation on this topic a few years ago, I was very happy to see (some of) his work on the topic published in such a widely read journal.

Evan also tells me that he has another paper on the topic titled “Behavior Genetics and Postgenomics” that’s forthcoming in Behavioral and Brain Sciences. Here is the abstract of that forthcoming piece:

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Published March 19, 2012

Slides of My Keynote Lecture at Last Weekend’s “Economics and Management of Risk in Agriculture and Natural Resources” Conference

I was trained as an agricultural and applied economist, so I have spent a lot of time doing research on risk as it relates to agriculture and development (see here and here for published articles).

Because of this, I have been involved with the annual Economics and Management of Risk in Agriculture and Natural Resources conference for the past few years.

I first presented at that conference in 2009, and since I had then volunteered to organize the conference, I was in charge of the conference program in 2010 and of logistics in 2011.

This year, I was asked to give the keynote lecture, in which I chose to discuss what the “credibility revolution” that took place in economics over the past ten years or so — which has lead to economists to adopting stricter standards of evidence and of statistical identification — means for agricultural and applied economics as a field.

In case you have an interest in this topic, I am making the slides of my keynote lecture are available. I think the content of those slides is especially relevant for current graduate students of agricultural and applied economics.

The Economics and Management of Risk in Agriculture and Natural Resources conference is usually held somewhere on the Gulf Coast. This year, it was held in Pensacola, FL. I took the picture on top of this post while walking along the beach early Saturday morning.

Published March 15, 2012

Experiments in Political Science

Two interesting articles were published within a few days of one another last week on the topic of experimental methods in political science.

The first article is by Jasjeet S. Sekhon and Rocio Titiunik in the American Political Science Review, and it discusses the uses and misuses of natural experiments:

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties.

In other words, many of the natural experiments found in the literature do not allow identifying causal effects, and the authors do a good job of providing examples of four published natural experiments whose findings they question. The findings in Sekhon and Titiunik’s article apply to some regression discontinuity designs as well.

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