  Quantile regression and quantile treatment effect methods are powerful
econometric tools for considering economic impacts of events or variables of
interest beyond the mean. The use of quantile methods allows for an examination
of impacts of some independent variable over the entire distribution of
continuous dependent variables. Measurement in many quantative settings in
economic history have as a key input continuous outcome variables of interest.
Among many other cases, human height and demographics, economic growth,
earnings and wages, and crop production are generally recorded as continuous
measures, and are collected and studied by economic historians. In this paper
we describe and discuss the broad utility of quantile regression for use in
research in economic history, review recent quantitive literature in the field,
and provide an illustrative example of the use of these methods based on 20,000
records of human height measured across 50-plus years in the 19th and 20th
centuries. We suggest that there is considerably more room in the literature on
economic history to convincingly and productively apply quantile regression
methods.
