“Age Heaping,” Numeracy, and Human Capital
| Peter Klein |
Important economic and managerial concepts like social capital and human capital, as latent variables, are difficult to discern in aggregate or historical data. My very clever friend (and occasional O&M commenter) Brian A’Hearn suggests that “age-heaping” — rounding up or down one’s self-reported age to the nearest five or zero — may be a good proxy for human capital. From Brian’s paper with Jörg Baten and Dorothee Crayen, “Quantifying Quantitative Literacy: Age Heaping and the History of Human Capital”:
As signature ability can proxy for literacy, so accuracy of age awareness can proxy for numeracy, and for human capital more generally. A society in which individuals know their age only approximately is a society in which life is governed not by the calendar and the clock but by the seasonal cycle, in which birth dates are not recorded by families or authorities, in which numerical age is not a criterion for access to privileges (e.g. voting, office-holding, marriage, holy orders) or for the imposition of responsibilities (such as military service or taxation), in which individuals who know their birth year have difficulty accurately calculating their age from the current year. Within a society, the least educated and those with the least interaction with state, religious, or other administrative bureaucracies will be least likely to know their age accurately. Age awareness thus tells us something about both the individual and he society he or she inhabits. Approximation in age awareness manifests itself in the phenomenon of age heaping in self-reported age data. Individuals lacking certain knowledge of their age rarely state this openly, but choose instead a figure they deem plausible. They do not choose randomly, but have a systematic tendency to prefer “attractive” numbers, such as those ending in 5 or 0, or even numbers, or in some societies numbers with other specific terminal digits. Age heaping can be assessed from any sufficiently numerous source of age data: census returns, tombstones, necrologies, muster lists, legal records, or tax data, for example. While care must be exercised in ascertaining possible biases, such data are in principle available much more widely than signature rates and other proxies for human capital.
Brian and his coauthors use age-heaping data to generate estimates of human capital in Europe over a long period of time, finding substantial increases in human capital just before the Industrial Revolution.