Calculating the Future: Capitalism and Predictive Statistics
This session bridges the history of capitalism and the history of science to examine how quantification and forecasting have become central to economic life in the United States and Britain from the eighteenth to the twentieth centuries. Predictive statistics have served crucial functions in capitalist economies and have been used to facilitate investment and development; influence commodity exchange and speculation; map flows of capital, credit, and commodities; anticipate and mitigate risk; manage work processes and workers; and coordinate shipping, distributing, and marketing to consumers. Yet despite this capacity to rationalize the economy, predictive statistics have often been a contested mode of knowledge production marked by inaccuracy and uncertainty. This session will focus on the economic and epistemological implications of trusting in numbers in a capitalist system.
Building on recent scholarship on forecasting that has begun to explore the relationship between knowledge production and markets, this session poses the following questions: How have institutions and individuals performed labors of quantification and forecasting? What kinds of bureaucratic structures and practices have emerged to produce predictive statistics? How have predictive statistics circulated through economies and cultures, and how has their meaning and value changed in so doing? How has the politics of access to predictive statistics affected different social groups? How have producers and consumers of predictive statistics conceptualized the economy, the environment, the nation, or the population? How has quantification both tamed and perpetuated economic, environmental, and demographic uncertainty?
This session’s papers will take up these questions in contexts ranging from the history of public credit in eighteenth-century Britain to the production of climatological data in the West Indies ca. 1898 to the history of the Dow Jones Industrial Average in the late-nineteenth and early-twentieth-century United States to the American life insurance industry’s statistical population forecasts in the mid-twentieth century. In a paper on Robert Walpole’s “Sinking Fund” and the intended repayment of national debt after the South Sea Bubble, William Deringer explores the relationship between predictive financial and political calculations and reveals a moment when the concept of public credit became defined in mathematical terms. Jamie Pietruska’s paper on the United States Weather Bureau’s predictive climatological data in the West Indies ca. 1898 analyzes the contested quantification of environmental knowledge in order to illuminate the role of government science in American empire as well as the technical and bureaucratic labors of quantification. In a paper on the emergence of the Dow Jones Industrial Average and social quantification, Eli Cook emphasizes the role of the business press in transforming statistical economic indicators into a barometer of American progress and economic well-being. Dan Bouk’s paper on predictive demographic tools and post-Depression fears of American biological stagnation uncovers a forgotten era before the “baby boom” that would have an important legacy in shaping debates over the welfare state and popularizing statistical population forecasts.