The effect of plough agriculture on gender roles

A machine learning approach

Bibliographic Data

ID21653418
AuthorsAnna Baiardi (Erasmus School of Economics Erasmus University and Tinbergen Institute Rotterdam Netherlands), Andrea A Naghi (Department of Business Analytics and Applied Economics Queen Mary University of London London UK, corresponding author)
Year2024
Volume39
Issue7
Pages1396-1402
Publication date2024-11-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.3083
OpenAlexW4400513147
LanguageEN
Citations received1
References cited14

This paper undertakes a replication in a wide sense of a recent study that examines the relationship between historical plough agriculture and current gender roles. We revisit the main research question with recently developed causal machine learning methods, which allow researchers to model the relationship of covariates with the treatment and the outcomes in a more flexible way, while also including interactions and nonlinearities that were not considered in the original analysis. Our results suggest an even larger negative effect of the historical plough adoption on female labor force participation than what the original analysis found. The paper highlights the benefits of using causal machine learning methods in applied empirical economics

Agricultural engineering · Agriculture · Agronomy · Biology · Geography · Machine learning · Plough · Agricultural Innovations and Practices · Agricultural risk and resilience · Animal Disease Management and Epidemiology · Computer Science · Engineering · Psychology

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Open DOIOpen Access