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The effect of plough agriculture on gender roles: A machine learning approach

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  • Anna Baiardi
  • Andrea A. Naghi

Abstract

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.

Suggested Citation

  • Anna Baiardi & Andrea A. Naghi, 2024. "The effect of plough agriculture on gender roles: A machine learning approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1396-1402, November.
    • Handle: RePEc:wly:japmet:v:39:y:2024:i:7:p:1396-1402
    DOI: 10.1002/jae.3083
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    References listed on IDEAS

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    Cited by:

    1. Anna Baiardi & Paul S. Clarke & Andrea A. Naghi & Annalivia Polselli, 2026. "Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications," Papers 2603.20464, arXiv.org.
    2. Zhang, Shengwu & Han, Liyan, 2026. "Low-altitude infrastructure and economic growth: Evidence from general aviation airports," Transport Policy, Elsevier, vol. 175(C).
    3. Apoorva Lal & Winston Chou, 2025. "Estimating Representative Causal Effects with Double Machine Learning," Papers 2506.07462, arXiv.org, revised Jun 2026.

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