Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection
Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Uehleke, Reinhard [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Land use policy - Son, Yang-Ju ELSEVIER, 2021, the international journal covering all aspects of land use, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:114 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.landusepol.2021.105950 |
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ELV056688458 |
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| 520 | |a Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. | ||
| 520 | |a Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. | ||
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10.1016/j.landusepol.2021.105950 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001695.pica (DE-627)ELV056688458 (ELSEVIER)S0264-8377(21)00673-6 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Uehleke, Reinhard verfasserin aut Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Q24 Elsevier Q58 Elsevier Q18 Elsevier D04 Elsevier Petrick, Martin oth Hüttel, Silke oth Enthalten in Elsevier Science Son, Yang-Ju ELSEVIER Land use policy 2021 the international journal covering all aspects of land use Amsterdam [u.a.] (DE-627)ELV006296785 volume:114 year:2022 pages:0 https://doi.org/10.1016/j.landusepol.2021.105950 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 114 2022 0 |
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10.1016/j.landusepol.2021.105950 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001695.pica (DE-627)ELV056688458 (ELSEVIER)S0264-8377(21)00673-6 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Uehleke, Reinhard verfasserin aut Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Q24 Elsevier Q58 Elsevier Q18 Elsevier D04 Elsevier Petrick, Martin oth Hüttel, Silke oth Enthalten in Elsevier Science Son, Yang-Ju ELSEVIER Land use policy 2021 the international journal covering all aspects of land use Amsterdam [u.a.] (DE-627)ELV006296785 volume:114 year:2022 pages:0 https://doi.org/10.1016/j.landusepol.2021.105950 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 114 2022 0 |
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10.1016/j.landusepol.2021.105950 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001695.pica (DE-627)ELV056688458 (ELSEVIER)S0264-8377(21)00673-6 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Uehleke, Reinhard verfasserin aut Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Q24 Elsevier Q58 Elsevier Q18 Elsevier D04 Elsevier Petrick, Martin oth Hüttel, Silke oth Enthalten in Elsevier Science Son, Yang-Ju ELSEVIER Land use policy 2021 the international journal covering all aspects of land use Amsterdam [u.a.] (DE-627)ELV006296785 volume:114 year:2022 pages:0 https://doi.org/10.1016/j.landusepol.2021.105950 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 114 2022 0 |
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10.1016/j.landusepol.2021.105950 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001695.pica (DE-627)ELV056688458 (ELSEVIER)S0264-8377(21)00673-6 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Uehleke, Reinhard verfasserin aut Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Q24 Elsevier Q58 Elsevier Q18 Elsevier D04 Elsevier Petrick, Martin oth Hüttel, Silke oth Enthalten in Elsevier Science Son, Yang-Ju ELSEVIER Land use policy 2021 the international journal covering all aspects of land use Amsterdam [u.a.] (DE-627)ELV006296785 volume:114 year:2022 pages:0 https://doi.org/10.1016/j.landusepol.2021.105950 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 114 2022 0 |
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10.1016/j.landusepol.2021.105950 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001695.pica (DE-627)ELV056688458 (ELSEVIER)S0264-8377(21)00673-6 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Uehleke, Reinhard verfasserin aut Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. Q24 Elsevier Q58 Elsevier Q18 Elsevier D04 Elsevier Petrick, Martin oth Hüttel, Silke oth Enthalten in Elsevier Science Son, Yang-Ju ELSEVIER Land use policy 2021 the international journal covering all aspects of land use Amsterdam [u.a.] (DE-627)ELV006296785 volume:114 year:2022 pages:0 https://doi.org/10.1016/j.landusepol.2021.105950 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 114 2022 0 |
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Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection |
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Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. |
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Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. |
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Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation. |
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