The Use of Quantile Methods in Economic History

TL;DR

This paper discusses the application of quantile regression methods in economic history, highlighting their ability to analyze the entire distribution of continuous variables and demonstrating their utility with historical height data.

Contribution

It reviews recent literature and advocates for broader adoption of quantile regression techniques in economic history research.

Findings

Quantile methods provide insights beyond average effects.

Historical height data can be effectively analyzed using quantile regression.

There is significant potential for more widespread use of these methods in economic history.

Abstract

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.