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 quantitative 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 quantitative literature in the field, point to potential limits in its use, 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, despite limitations in certain settings, there is still considerably more room in the literature on economic history to convincingly and productively apply quantile regression methods.

摘要

分位数回归和分位数处理效应方法是强大的计量经济学工具，用于考虑超出均值的事件或感兴趣变量的经济影响。使用分位数方法可以检查某些自变量对连续因变量的整个分布的影响。经济史上许多定量背景下的测量都将感兴趣的连续结果变量作为关键输入。在许多其他情况下，人类身高和人口统计、经济增长、收入和工资以及作物产量通常被记录为连续的衡量标准，并由经济历史学家收集和研究。在本文中，我们描述和讨论了分位数回归在经济史研究中的广泛用途，回顾了该领域最近的定量文献，指出了其使用的潜在限制，并根据 19 世纪和 20 世纪 50 多年来测量的 20,000 条人类身高记录，提供了使用这些方法的说明性示例。我们认为，尽管在某些情况下存在局限性，经济史文献中仍有相当大的空间可以令人信服地、有效地应用分位数回归方法。