At Least He Spelled "Fogel" Correctly
Nicholas Wapshott is the British journalist who produced 2011's Keynes-Hayek: The Clash that Defined Modern Economics, an entertaining but ephemeral tale of Cambridge in the 1930s (here is John Cochran's less-than-flattering review). Wapshott sees himself as an important critic of "free-market orthodoxy," but his grasp of economic theory, history, and policy is more than a bit muddled. His newest article, a tribute to the late Robert Fogel, is positioned as a defense of cliometrics, the application of econometric techniques to economic history (and a critique of — well, he doesn't say, exactly, but presumably Austrian-style deductive analysis wouldn't pass muster). Unfortunately, the article is riddled with howlers.
Wapshott describes Hayek, author of "Why I Am Not a Conservative," as a "conservative saint" and calls Abba Lerner, one of Fogel's teachers, an "Austrian School economist." Fogel's cliometric approach is described as a "data mining," a somewhat loose term that generally applies to inductive analysis, not the kind of hypothesis testing associated with cliometrics. Wapshott takes time to praise Simon Kuznets, "whose pioneering work in econometrics led to the accurate measurement of economic growth." Kuznets's work was certainly pioneering, as in novel, but no respectable development economist thinks GDP is an "accurate" measure of national well-being, let along economic welfare, and its limits are well known in the growth accounting literature.
The funniest line, however is this: "Fogel was one of the best sort of economists, like Milton Friedman and Anna Schwartz, devoted to determining cause and effect through a meticulous study of the facts." Only the most naive empirical social scientist — or journalist without any actual research experience — could believe that causality emerges from "studying the facts." To be sure, modern empirical economics is obsessed with causal inference, but no skilled econometrician thinks that the various popular identification techniques "determine" cause and effect. The so-called identification revolution is not without critics, and many practitioners worry that it has gone too far.