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Predicting Farms’ Noncompliance with Regulations on Nitrate Pollution. ESRI WP609, January 2019

Lunn, Pete and Lyons, Seán and Murphy, Martin (2019) Predicting Farms’ Noncompliance with Regulations on Nitrate Pollution. ESRI WP609, January 2019. [Working Paper]

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    Abstract

    Despite ongoing efforts by regulatory authorities, there is significant noncompliance with the EU Nitrates Directive among farms in Ireland. Nutrient pollution harms water quality and ecosystems, and farms are subject to fines for noncompliance. This paper examines reasons for noncompliance and develops methods to predict which farms have the highest probability of being in breach of the Nitrates Regulations. We estimate econometric models of noncompliance using rich administrative data on farm and farmer characteristics collected by Ireland’s Department of Agriculture. We identify significant relationships between farm characteristics and the odds of a farm exceeding regulatory limits. We also find that econometric models can predict exceedances more accurately than a regulatory rule-of-thumb that flags farms with nitrates levels above a set threshold in the previous year. This approach illustrates the potential benefits of using statistical analysis of administrative data to assist regulatory enforcement when behavioural factors are involved.

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    Item Type: Working Paper
    Subjects for non-EU documents: EU policies and themes > Policies & related activities > agriculture policy
    EU policies and themes > Policies & related activities > environmental policy (including international arena)
    Countries > Ireland
    Subjects for EU documents: UNSPECIFIED
    EU Series and Periodicals: UNSPECIFIED
    EU Annual Reports: UNSPECIFIED
    Series: Series > Economic and Social Research Institute (ESRI), Dublin > ESRI Working Papers
    Depositing User: Phil Wilkin
    Official EU Document: No
    Language: English
    Date Deposited: 26 Dec 2019 13:46
    Number of Pages: 22
    Last Modified: 26 Dec 2019 13:46
    URI: http://aei.pitt.edu/id/eprint/102222

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