A few days ago I received in the snail mail a copy of Robert Fogel's new book, Political Arithmetic: Simon Kuznets and the Empirical Tradition in Economics. Maybe it was in my box for a while; I must admit to checking it only infrequently. (I mean, seriously, what of interest arrives anymore by U.S. mail? Someone should invent a mailbox that flushes.) I'm also not sure who sent it; maybe it was Fogel, or maybe it was the NBER or the University of Chicago Press, as the book is in the legendary NBER Series on Long-Term Factors in Economic Development, published by the U. of C. Press.
Anyway, Fogel's book is fantastic.
First, it's just a little longer than one hundred pages. I appreciate that.
Second, I personally like it because it's related to my alma mater and employer, the University of Pennsylvania, where Kuznets taught for decades, and about measurement in economics, which I view as central. Many people associate Kuznets with Harvard, which is also correct, but Harvard appointments tend to be lagging indicators -- effectively rewards for earlier path-breaking work done elsewhere -- and in Kuznets' case the earlier path-breaking work was done at the University of Pennsylvania. Moreover, many of Penn's most-lauded subsequent contributions have also been in the Kuznets' empirical / measurement tradition, from the early macro-econometric models of Nobel laureate Larry Klein, to the ongoing Penn World Tables of Irv Kravis, Bob Summers and Al Heston, to a wide variety of more recent work by current faculty. (Hey, if Greg Mankiw can gush about Harvard in his blog, then I can gush about Penn in mine.)
Third, it's a beautifully-written and entertaining history of many fascinating and entangled aspects of economic measurement, from the rise of academic economics in the early twentieth century, to the development of the NBER's stunningly-successful tradition of empirical economics, to the interplay between measurement and theory. I found it especially valuable and informative in that, although the NBER's tradition in empirical business cycle analysis (Burns, Mitchell, Zarnowitz, Stock and Watson, etc.) is well-known and justly lauded, the parallel NBER tradition in empirical growth analysis is less well understood and appreciated (certainly by me, but I suspect much more widely).
Fourth, it's eye opening. We tend to take things like GDP data for granted, as with the national income and product accounts more generally. But before Kuznets' path-breaking work, there was nothing. Imagine that. Seriously, try to imagine that! Imagine trying to price an asset like foreign exchange with no idea of domestic or foreign GDP growth or inflation, or more generally, imagine trying to make any kind of economic or financial decision -- from the national level to the household level -- largely in a data vacuum. That's how it was, less than a hundred years ago. Thanks, Simon, for shining your light.
Finally, the book, like Kuznets' work itself, testifies convincingly to the power and centrality of measurement in science. Indeed measurement is the essence of science. Yes, yes, of course, my offended theorist friends, theory is important too, but theory is little more than data reduction, and the theory mill needs grist before it can grind. And yes, I'll have more to say about measurement vs. theory in future installments.
But for now, there's only one important matter at hand -- thanking Robert Fogel for writing his beautiful and unique book. Here here!