The use of quantile methods in economic history

Damian Clarke, Manuel Llorca Jaña, Daniel Pailañir

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)115-132
Number of pages18
JournalHistorical Methods
Issue number2
StatePublished - 2023
Externally publishedYes


  • B41
  • C21
  • C22
  • N30
  • Quantile regression
  • economic history
  • practitioners
  • quantile treatment effects


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