Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018
Gespeichert in:
| Autor*in: |
Uehleke, Reinhard [verfasserIn] Leonhardt, Heidi [verfasserIn] Hüttel, Silke - 1978- [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Agricultural economics - Oxford : Blackwell, 1986, 55(2024), 1 vom: Jan., Seite 27-40 |
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| Übergeordnetes Werk: |
volume:55 ; year:2024 ; number:1 ; month:01 ; pages:27-40 |
| Links: |
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| DOI / URN: |
10.1111/agec.12805 |
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| Katalog-ID: |
1881337030 |
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| 982 | |2 26 |1 00 |x DE-206 |b 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. | ||
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10.1111/agec.12805 doi (DE-627)1881337030 (DE-599)KXP1881337030 DE-627 ger DE-627 rda eng Uehleke, Reinhard verfasserin aut Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 Leonhardt, Heidi verfasserin aut Hüttel, Silke 1978- verfasserin (DE-588)141157488 (DE-627)625403533 (DE-576)322477239 aut Enthalten in Agricultural economics Oxford : Blackwell, 1986 55(2024), 1 vom: Jan., Seite 27-40 Online-Ressource (DE-627)306652080 (DE-600)1499675-3 (DE-576)081984324 1574-0862 nnns volume:55 year:2024 number:1 month:01 pages:27-40 https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 Verlag kostenfrei https://doi.org/10.1111/agec.12805 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 55 2024 1 1 27-40 26 01 0206 4489488432 x1z 21-02-24 2403 01 DE-LFER 4497093328 00 --%%-- --%%-- n --%%-- l01 07-03-24 2403 01 DE-LFER https://doi.org/10.1111/agec.12805 2403 01 DE-LFER https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 26 00 DE-206 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. |
| spelling |
10.1111/agec.12805 doi (DE-627)1881337030 (DE-599)KXP1881337030 DE-627 ger DE-627 rda eng Uehleke, Reinhard verfasserin aut Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 Leonhardt, Heidi verfasserin aut Hüttel, Silke 1978- verfasserin (DE-588)141157488 (DE-627)625403533 (DE-576)322477239 aut Enthalten in Agricultural economics Oxford : Blackwell, 1986 55(2024), 1 vom: Jan., Seite 27-40 Online-Ressource (DE-627)306652080 (DE-600)1499675-3 (DE-576)081984324 1574-0862 nnns volume:55 year:2024 number:1 month:01 pages:27-40 https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 Verlag kostenfrei https://doi.org/10.1111/agec.12805 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 55 2024 1 1 27-40 26 01 0206 4489488432 x1z 21-02-24 2403 01 DE-LFER 4497093328 00 --%%-- --%%-- n --%%-- l01 07-03-24 2403 01 DE-LFER https://doi.org/10.1111/agec.12805 2403 01 DE-LFER https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 26 00 DE-206 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. |
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10.1111/agec.12805 doi (DE-627)1881337030 (DE-599)KXP1881337030 DE-627 ger DE-627 rda eng Uehleke, Reinhard verfasserin aut Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 Leonhardt, Heidi verfasserin aut Hüttel, Silke 1978- verfasserin (DE-588)141157488 (DE-627)625403533 (DE-576)322477239 aut Enthalten in Agricultural economics Oxford : Blackwell, 1986 55(2024), 1 vom: Jan., Seite 27-40 Online-Ressource (DE-627)306652080 (DE-600)1499675-3 (DE-576)081984324 1574-0862 nnns volume:55 year:2024 number:1 month:01 pages:27-40 https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 Verlag kostenfrei https://doi.org/10.1111/agec.12805 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 55 2024 1 1 27-40 26 01 0206 4489488432 x1z 21-02-24 2403 01 DE-LFER 4497093328 00 --%%-- --%%-- n --%%-- l01 07-03-24 2403 01 DE-LFER https://doi.org/10.1111/agec.12805 2403 01 DE-LFER https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 26 00 DE-206 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. |
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10.1111/agec.12805 doi (DE-627)1881337030 (DE-599)KXP1881337030 DE-627 ger DE-627 rda eng Uehleke, Reinhard verfasserin aut Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 Leonhardt, Heidi verfasserin aut Hüttel, Silke 1978- verfasserin (DE-588)141157488 (DE-627)625403533 (DE-576)322477239 aut Enthalten in Agricultural economics Oxford : Blackwell, 1986 55(2024), 1 vom: Jan., Seite 27-40 Online-Ressource (DE-627)306652080 (DE-600)1499675-3 (DE-576)081984324 1574-0862 nnns volume:55 year:2024 number:1 month:01 pages:27-40 https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 Verlag kostenfrei https://doi.org/10.1111/agec.12805 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 55 2024 1 1 27-40 26 01 0206 4489488432 x1z 21-02-24 2403 01 DE-LFER 4497093328 00 --%%-- --%%-- n --%%-- l01 07-03-24 2403 01 DE-LFER https://doi.org/10.1111/agec.12805 2403 01 DE-LFER https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 26 00 DE-206 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. |
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10.1111/agec.12805 doi (DE-627)1881337030 (DE-599)KXP1881337030 DE-627 ger DE-627 rda eng Uehleke, Reinhard verfasserin aut Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 Leonhardt, Heidi verfasserin aut Hüttel, Silke 1978- verfasserin (DE-588)141157488 (DE-627)625403533 (DE-576)322477239 aut Enthalten in Agricultural economics Oxford : Blackwell, 1986 55(2024), 1 vom: Jan., Seite 27-40 Online-Ressource (DE-627)306652080 (DE-600)1499675-3 (DE-576)081984324 1574-0862 nnns volume:55 year:2024 number:1 month:01 pages:27-40 https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 Verlag kostenfrei https://doi.org/10.1111/agec.12805 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 55 2024 1 1 27-40 26 01 0206 4489488432 x1z 21-02-24 2403 01 DE-LFER 4497093328 00 --%%-- --%%-- n --%%-- l01 07-03-24 2403 01 DE-LFER https://doi.org/10.1111/agec.12805 2403 01 DE-LFER https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 26 00 DE-206 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. |
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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. 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26 00 DE-206 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 Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 Reinhard Uehleke, Heidi Leonhardt, Silke Hüttel agri-environmental schemes (dpeaa)DE-206 DID matching (dpeaa)DE-206 double machine learning (dpeaa)DE-206 policy impact evaluation (dpeaa)DE-206 |
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| title_short |
Counterfactual evaluation of two Austrian agri-environmental schemes in 2014-2018 |
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https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805 https://doi.org/10.1111/agec.12805 |
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Leonhardt, Heidi Hüttel, Silke 1978- |
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10.1111/agec.12805 |
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| up_date |
2024-07-04T14:52:26.403Z |
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code="r">https://doi.org/10.1111/agec.12805</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://onlinelibrary.wiley.com/doi/pdf/10.1111/agec.12805</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">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.</subfield></datafield></record></collection>
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7.3987417 |