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How Differently Do Farms Respond to Agri-environmental Policies? A Probabilistic Machine-Learning Approach

Author

Listed:
  • Silvia Coderoni
  • Roberto Esposti
  • Alessandro Varacca

Abstract

This study evaluates the extent to which farmers respond heterogeneously to the agri-environmental policies implemented in the European Common Agricultural Policy (CAP). Our identification and estimation strategy combines a theory-driven research design formalizing all possible sources of heterogeneity with a Bayesian additive regression trees algorithm. Results from a 2015–2018 panel of Italian farms show that the responsiveness to these policies may differ substantially across farms and farm groups. This suggests room for improvement in implementing these policies. We also argue that the specific features of the CAP call for a careful implementation of these empirical techniques.

Suggested Citation

  • Silvia Coderoni & Roberto Esposti & Alessandro Varacca, 2024. "How Differently Do Farms Respond to Agri-environmental Policies? A Probabilistic Machine-Learning Approach," Land Economics, University of Wisconsin Press, vol. 100(2), pages 370-397.
    • Handle: RePEc:uwp:landec:v:100:y:2024:i:2:p:370-397
    Note: DOI: https://doi.org/10.3368/le.100.2.060622-0043R1
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    References listed on IDEAS

    as
    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    3. J. A. Finn & F. Bartolini & D. Bourke & I. Kurz & D. Viaggi, 2009. "Ex post environmental evaluation of agri-environment schemes using experts' judgements and multicriteria analysis," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 52(5), pages 717-737.
    4. David S. Yeager & Paul Hanselman & Gregory M. Walton & Jared S. Murray & Robert Crosnoe & Chandra Muller & Elizabeth Tipton & Barbara Schneider & Chris S. Hulleman & Cintia P. Hinojosa & David Paunesk, 2019. "A national experiment reveals where a growth mindset improves achievement," Nature, Nature, vol. 573(7774), pages 364-369, September.
    5. Linda Arata & Paolo Sckokai, 2016. "The Impact of Agri-environmental Schemes on Farm Performance in Five E.U. Member States: A DID-Matching Approach," Land Economics, University of Wisconsin Press, vol. 92(1), pages 167-186.
    6. Edoardo Baldoni & Silvia Coderoni & Roberto Esposti, 2021. "Immigrant workforce and agriculture productivity: evidence from Italian farm-level data [Fractionalization]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 805-834.
    7. Ehlers, Melf-Hinrich & Huber, Robert & Finger, Robert, 2021. "Agricultural policy in the era of digitalisation," Food Policy, Elsevier, vol. 100(C).
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    9. Kapelner, Adam & Bleich, Justin, 2016. "bartMachine: Machine Learning with Bayesian Additive Regression Trees," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i04).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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