IDEAS home Printed from https://ideas.repec.org/a/bla/agecon/v55y2024i1p27-40.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/agec.12805
    Download Restriction: no
    File URL: https://libkey.io/10.1111/agec.12805?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wooldridge, Jeffrey M., 2016. "Should instrumental variables be used as matching variables?," Research in Economics, Elsevier, vol. 70(2), pages 232-237.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. repec:adr:anecst:y:2008:i:91-92:p:10 is not listed on IDEAS
    15. 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.
    16. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uehleke, Reinhard & Petrick, Martin & Hüttel, Silke, 2022. "Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection," Land Use Policy, Elsevier, vol. 114(C).
    2. Michalek, Jerzy, 2022. "Environmental and farm impacts of the EU RDP agri-environmental measures: Evidence from Slovak regions," Land Use Policy, Elsevier, vol. 113(C).
    3. Cisilino, Federica & Bodini, Antonella & Zanoli, Agostina, 2019. "Rural development programs’ impact on environment: An ex-post evaluation of organic faming," Land Use Policy, Elsevier, vol. 85(C), pages 454-462.
    4. Roggendorf, Wolfgang & Schwarze, Stefan, 2020. "Die Wirkung von Agrarumweltmaßnahmen auf betriebliche Stickstoffbilanzen – Empirische Ergebnisse aus Nordrhein-Westfalen," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305604, German Association of Agricultural Economists (GEWISOLA).
    5. Roggendorf, Wolfgang & Schwarze, Stefan, 2020. "Die Wirkung von Agrarumweltmaßnahmen auf betriebliche Stickstoffbilanzen – Empirische Ergebnisse aus Nordrhein-Westfalen," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305604, German Association of Agricultural Economists (GEWISOLA).
    6. Cristina SALVIONI & Dario SCIULLI, 2018. "Rural development policy in Italy: the impact of growth-oriented measures on farm outcomes," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(3), pages 115-130.
    7. Amer Ait Sidhoum & Carolin Canessa & Johannes Sauer, 2023. "Effects of agri‐environment schemes on farm‐level eco‐efficiency measures: Empirical evidence from EU countries," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 551-569, June.
    8. Bertoni, Danilo & Curzi, Daniele & Aletti, Giacomo & Olper, Alessandro, 2020. "Estimating the effects of agri-environmental measures using difference-in-difference coarsened exact matching," Food Policy, Elsevier, vol. 90(C).
    9. Bartolini, Fabio & Vergamini, Daniele & Longhitano, Davide & Povellato, Andrea, 2021. "Do differential payments for agri-environment schemes affect the environmental benefits? A case study in the North-Eastern Italy," Land Use Policy, Elsevier, vol. 107(C).
    10. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
    11. Amer Ait Sidhoum & Philipp Mennig & Johannes Sauer, 2023. "Do agri-environment measures help improve environmental and economic efficiency? Evidence from Bavarian dairy farmers," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(3), pages 918-953.
    12. Chabé-Ferret, Sylvain, 2017. "Should We Combine Difference In Differences with Conditioning on Pre-Treatment Outcomes?," TSE Working Papers 17-824, Toulouse School of Economics (TSE).
    13. Laure Kuhfuss & Julie Subervie, 2015. "Do agri-environmental schemes help reduce herbicide use? Evidence from a natural experiment in France," Post-Print hal-01199067, HAL.
    14. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
    15. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    16. Duncan Chaplin & Arif Mamun & Ali Protik & John Schurrer & Divya Vohra & Kristine Bos & Hannah Burak & Laura Meyer & Anca Dumitrescu & Christopher Ksoll & Thomas Cook, "undated". "Grid Electricity Expansion in Tanzania by MCC: Findings from a Rigorous Impact Evaluation, Final Report," Mathematica Policy Research Reports 144768f69008442e96369195e, Mathematica Policy Research.
    17. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    18. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    19. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    20. Chabé-Ferret, Sylvain & Voia, Anca, 2019. "Are Grassland Conservation Programs a Cost-Effective Way to Fight Climate Change? Evidence from France," SocArXiv cx8j6, Center for Open Science.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:agecon:v:55:y:2024:i:1:p:27-40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.