Chapter 2 highlights the use and effects of Solow’s model as an instrument of measurement. It relates the model to the postwar politics of growth and productivity and a line of inquiry that sought to gauge the national whole in terms of monetary units. Existing measurement practices at the National Bureau of Economic Research (NBER) involved the activities of collecting, compiling, and processing data; its researchers complemented and qualified their numbers with descriptive, verbal accounts about how the data had been made and how different measurement procedures led to different results. Here, the model reordered knowledge and nonknowledge about productivity. While commentators were shocked by its utter constructivism and disregard for the ways data were made, it offered a seemingly clean-cut method of measurement that turned statistical inference into a technical procedure. Whatever the model’s neoclassical reading of numbers did not account for was efficiently stashed away in a residual term labeled “technical change.” While Solow explicitly noted that the rest captured all kinds of (relevant) things, the technique remained and was soon denoted “the Solow residual.”

The introduction provides an overview of the book. Although Paul Douglas is the most important person in the story of the Cobb–Douglas regression, the book is not a biography of Douglas, but of his best known and most successful creation. The book covers the period from the mid-1920s, when Douglas was doing the statistical work that led to the first use of the Cobb–Douglas regression, to the late 1960s, when Douglas’s idea had come to be widely accepted by economists, and the CES production function was emerging as the first popular alternative to the Cobb–Douglas function for estimation of production functions. My perspective is mainly that of a historian of the use of statistical analysis in economics, with particular attention given to the challenges faced by empirical researchers, and the roles played by statistical and economic theory in their development of strategies to overcome them. At various point in the book, I reflect on factors that might have helped the production function regression go from an innovative and controversial statistical procedure to a widely accepted general purpose tool in empirical economics.