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Counterfactual evaluation of two Austrian agri‐environmental schemes in 2014–2018

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  • Reinhard Uehleke
  • Heidi Leonhardt
  • Silke Hüttel

Abstract

This article investigates the causal effect of farm participation in two Austrian agri‐environmental schemes (AES), Immergrün (ground cover) and Zwischenfrucht (catch cropping), on fertilizer and plant protection expenditures in the 2014 programming period. Combining European Farm Accountancy Data Network data with information on scheme participation from administrative control data offers identifying farm participation in specific schemes targeted at reducing input intensity. Given the overall small sample, we maximized the utilizable sample size by combining difference‐in‐difference and kernel matching with automated bandwidth selection. To address the remaining post‐matching covariate imbalances, we used double machine learning (DML) techniques for a guided selection of potential confounding covariates. Our results suggest that, given the available sample, we cannot substantiate moderate effects of AES participation, and that guided covariate selection by DML offers no gain over non‐guided covariate selection for the small sample. Our results underline the need to increase the number of farms and the duration in available farm panels to substantiate future counterfactual‐based evaluations of policy.

Suggested Citation

  • Reinhard Uehleke & Heidi Leonhardt & Silke Hüttel, 2024. "Counterfactual evaluation of two Austrian agri‐environmental schemes in 2014–2018," Agricultural Economics, International Association of Agricultural Economists, vol. 55(1), pages 27-40, January.
    • Handle: RePEc:bla:agecon:v:55:y:2024:i:1:p:27-40
    DOI: 10.1111/agec.12805
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    References listed on IDEAS

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    1. 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.
    2. Hans Vrolijk & Krijn Poppe, 2021. "Cost of Extending the Farm Accountancy Data Network to the Farm Sustainability Data Network: Empirical Evidence," Sustainability, MDPI, vol. 13(15), pages 1-13, July.
    3. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    5. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    6. Andrea Pufahl & Christoph R. Weiss, 2009. "Evaluating the effects of farm programmes: results from propensity score matching," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 36(1), pages 79-101, March.
    7. Gregory Howard, 2020. "Additionality Violations in Agricultural Payment for Service Programs: Experimental Evidence," Land Economics, University of Wisconsin Press, vol. 96(2), pages 244-264.
    8. Chabé-Ferret, Sylvain & Subervie, Julie, 2013. "How much green for the buck? Estimating additional and windfall effects of French agro-environmental schemes by DID-matching," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 12-27.
    9. repec:adr:anecst:y:2008:i:91-92:p:10 is not listed on IDEAS
    10. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    11. Riccardo D’Alberto & Matteo Zavalloni & Meri Raggi & Davide Viaggi, 2018. "AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    12. Jose C. Galdo & Jeffrey Smith & Dan Black, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annals of Economics and Statistics, GENES, issue 91-92, pages 189-216.
    13. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    14. Wooldridge, Jeffrey M., 2016. "Should instrumental variables be used as matching variables?," Research in Economics, Elsevier, vol. 70(2), pages 232-237.
    15. Stefano Pascucci & Tiziana de-Magistris & Liesbeth Dries & Felice Adinolfi & Fabian Capitanio, 2013. "Participation of Italian farmers in rural development policy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(4), pages 605-631, September.
    16. Xinwei Ma & Jingshen Wang, 2020. "Robust Inference Using Inverse Probability Weighting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1851-1860, December.
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