(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Political scientist Brian Silver points me to his post by economist Paul Romer, who writes:
The style that I [Romer] am calling mathiness lets academic politics masquerade as science. Like mathematical theory, mathiness uses a mixture of words and symbols, but instead of making tight links, it leaves ample room for slippage between statements in natural versus formal language and between statements with theoretical as opposed to empirical content.
Also some thoughtful discussion by Leopoldo Fergusson, who writes:
In empirical work there are phenomena akin to mathiness, and similar risks. Mathiness stems from certain obsession, healthy to some extent, with formal economic analysis. Similarly, in empirical work many risks arise from a healthy concern about being more rigorous when analyzing data . . .
Economists (social scientists in general) obsessed with identifying the causal effect (yes, it is redundant and yet we love it) can fall into the trap of studying comparatively minor problems . . .
In his (otherwise great) article on writing advice for PhD students, John Cochrane asks: “What are the three most important things for empirical work?” His response: “Identification, Identification, Identification”.
Wrong. The most important thing, always, is that we tackle an interesting question. . . .
Regarding the general problem of “mathiness” serving as a deterrent to research communication in economics, this is an interesting point, especially in that in many ways political science has gone in the opposite direction. Back when I was getting my Ph.D., there were not many “political methodologists,” and there was a large overlap with the formal theorists. Game theory ruled, and the people who were considered the top methodologists were aping econometricians. But the field was young and malleable enough that things opened up: “formal theory” became a bit of a backwater (at least from my perspective) and statistical modeling and graphics became more popular. So, sure, math is cool, but it’s a rare work of political science that uses math to exclude dissenters.
Also I was amused by Romer’s earlier post, “Ed Prescott is No Robert Solow, No Gary Becker.” As far as I can tell, Gary Becker was no Gary Becker. As for Solow, I only saw him once, in a talk at MIT 30 years ago where he anti-impressed me by making an offhand swipe at how he would cut funding for Amtrak—I guess he thought all those highways were just free.
Silver replied with some background of his own:
I entered grad school in 1965 and started out as a “Russian area specialist.” That my dissertation was largely a quantitative study of ethnic assimilation using census data was very different from the norm that was established at the major Russian area centers at Harvard and Columbia as well as the significant ones elsewhere. When I applied for a Foreign Area Fellowship as well as a year of study abroad through IREX, the interview committee asked me about my thesis. I told them I was studying ethnic assimilation by minority nationalities in the Soviet Union. The immediate question: “WHICH nationality?” My answer: “all of them.” It shocked them that anybody could try to do that! (I got the fellowships.)
Only when the Soviet system began to fall apart did this subfield begin to draw a lot of young scholars into it who applied a wide array of methods, including quantitative, to study the post-Soviet transition. About a third of my research was essentially “demographic,” and not obviously political. Today there’s practically nobody in US doing this work concerning the post-Soviet region. There is, however, something of a normal demographic science now within Russia — but one that treads very carefully and doesn’t deal with some of the issues that were among the foci of my research (language and ethnic identity change, bilingual education policy, etc.).
For the most part the “comparativists” at Wisconsin—the faculty—were qualitatively oriented. But back in 1965 we got to cut our data analysis teeth in the introductory mass political behavior course by analyzing the Almond-Verba data, which had just been released. So some of us comparativists learned to do quantitative data analysis—and multi-country research. Nobody taught formal theory there at the time. When I asked one of the comparative faculty why this was so, he quickly responded, “We don’t believe in it.” But they did believe in data analysis, and so some of us comparativists got decent training even in political science, and a few (e.g., Doug Hibbs, who was in my UW cohort) took econometrics from Goldberger.
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