targeting monitoring resources to enhance the effectiveness of the cap

© The Agricultural Ecomomics Society and the European Association of Agricultural Economists 2005

From its inception the operation of the Common Agricultural Policy (CAP) has required an element of the compliance monitoring of farming activities in order to validate the claims made by farmers for commodity support payments. With the evolution of the CAP there is an ever-increasing need for the monitoring of farmers' behaviour to ensure compliance with program objectives. Recent examples include the introduction in 1992 of a set-aside requirement for arable farmers in return for associated payments, the numerous agri-environmental policies (AEP) (e.g., the new Environmental Stewardship Program in England, DEFRA (2005)) and the new EU Single Farm Payment (SFP) system with its associated cross-compliance requirements.

An important aspect of the reformed CAP is the increasing complexity of the monitoring functions required to assess whether farmers are, in the case of cross-compliance, keeping their land in good agricultural and environmental condition (GAEC). The practical diffi culties associated with

Targeting Monitoring Resources to Enhance the Effectiveness of the CAP

implementing and enforcing GAEC are the subject of much debate and analysis in policy circles (e.g., Kirkham et al., 2004). At the same the Commission of the European Communities (2005)

more generally acknowledges the need for better implementation of environmental regulation especially with respect to compliance monitoring and enforcement.

Cibler le contrôle des ressources pour améliorer l’efficacité de la PACDie Zielausrichtung von Überwachungsressourcen zur Effektivitätsverbesserung der GAP

Iain Fraser and Rob Fraser

‘Es gibt eine

überzeugende theore-tische Grundlage, die verstärkte Anwendung einer zielgerichte-ten Überwachung zur Einhaltung von Bestimmungen als Teil einer überarbeiteten und sich entwickelnden GAP zu unterstützen.

22 EuroChoices 4(3)★

4-3_03Fraser.indd 224-3_03Fraser.indd 22 30/11/2005 10:15:3730/11/2005 10:15:37


The aim of compliance monitoring activities has always been to keep non-compliant behaviour by farmers in relation to CAP payments to a minimum. However, CAP monitoring resources are limited, and as a consequence, the operation of the monitoring system has inevitably meant that the probability of being caught cheating is signifi cantly less than one. Moreover, anticipated increases in the complexity of the monitoring tasks with the introduction of the SFP will stretch limited monitoring resources even further. Farmers may realistically expect the probability of being caught cheating on CAP compliance requirements to decrease with these changes in the CAP. Therefore, the issue of using CAP monitoring

resources most effectively within a more complex cross-compliance policy context is quite rightly on the minds of policy-makers at this time.

Within the economics literature compliance monitoring – an important element in policy design – is typically analysed within a principal-agent framework (e.g. Choe and Fraser, 1999). The particular problem of cheating is typically referred to in this context as the moral hazard problem. Specifi cally, this can occur where an agent (i.e. a farmer) is offered a payment by a principal (i.e., the government) in return for the agent undertaking a costly activity (e.g., participating in AEP). If the agent has a probability of less than one of being detected for failing to undertake this costly activity, then the agent has an incentive to ‘cheat’ the principal, i.e. accept the payment but not undertake the activity.

A key issue of policy design for the principal is determining monitoring frequency and the penalty to deter non-compliance. If monitoring resources or the size of penalties are unlimited, then the problem of moral hazard will disappear. But typically a principal will face fi nancial constraints on the extent of monitoring resources as well as limited penalties. Therefore, the optimal choice of these variables to minimize moral hazard is a common,

practical policy problem. In this article we outline the scope for using targeting to enhance the effectiveness of monitoring resources in dealing with non-compliant behaviour.

What is targeting?

There are two aspects of targeting.

First, agents can be targeted based on past performance. In this case ‘poor performers’ are placed in a group that is subject to higher monitoring until performance improves. There are many examples of this approach to targeting of monitoring efforts. For example, Eckert (2004) provides such an analysis of regulatory behaviour with respect to petroleum storage in Manitoba.

Second, the principal announces in advance of the commencement of the monitoring process that one or more sub-groups of agents will be ‘targeted’. This implies a re-allocation of monitoring resources towards a particular sub-group, thereby increasing the probability of agents in this sub-group being monitored compared to all other agents. The rationale for this type of targeting is that the principal has a larger body of information on a particular sub-group that should increase the likelihood of detecting non-compliance. Agents

L’idée d’un recours

accru au contrôle ciblé dans l’implémenta-tion des prochaines versions de la PAC s’appuie sur des bases théoriques sérieuses.

23EuroChoices 4(3) ★



are typically pooled together in sub-groups on the basis of some set of common characteristics.

An example is self-assessment tax returns in Australia. The Australian Tax Offi ce (AT0) announces each year different groups of workers (e.g., teachers, nurses, etc.) who will be the subject of targeting. As a result, workers in these groups know that the probability of having a tax return audited in detail increases signifi cantly. The ATO approach to targeting particular groups is based on an analysis of compliance risks and they consider this approach to be a cost effective means by which to achieve their policy mission (ATO, 2005). Targeting has also been applied in the general area of environmental policy to deal with compliance violators (see Heyes, 2000 for a comprehensive and insightful review of the academic literature in this area).

By targeting scarce resources the principal can increase the set of information on particular groups making the identifi cation of inappropriate behaviour easier. In the case of the CAP AEP is well suited to the use of group targeting of monitoring resources because it is frequently geographical and farming system focussed, or it requires the undertaking of specifi c environmental management activities.

Using group targeting to enhance monitoring effectiveness

Targeting involves re-allocating monitoring resources so that the targeted sub-group of agents has a higher probability of being monitored (and therefore potentially of being caught cheating). Therefore, the penalty multiplied by the (perceived) probability of being caught (the expected value) for those agents in

the targeted sub-group increases. It is anticipated that this will deter some or all agents from choosing to try and cheat the principal.

However, the problem for the principal is that in reallocating monitoring resources towards the targeted sub-group, there is likely to be an associated reduction in resources allocated to monitoring compliance among non-targeted agents. It follows that agents in the non-target group are likely to experience both a reduction in the probability of being caught cheating, and as a consequence, an increase in the incentive to cheat. Therefore, it is unclear whether the effi ciency gains from reduced cheating among targeted agents will not be out-weighed by effi ciency losses associated with increased cheating by non-targeted agents.

How might we solve this problem? We could increase the amount of monitoring resources overall so as to prevent increased cheating among non-targeted agents. But it is unclear if the additional costs will yield suffi cient benefi ts. An alternative approach (Fraser, 2004) is that the principal draw on the concept of a ‘mean-penalty-preserving’ adjustment of its monitoring and penalty parameters. This adjustment aims to mitigate the impact of the reallocation of resources away from non-targeted agents on their incentive to cheat. Adopting this concept involves neither the expending of more monitoring resources by the principal, nor an increase in the expected penalty associated with being caught cheating for non-targeted agents. Rather, it balances the targeting-induced reduction in the probability

of being caught cheating for non-targeted agents with an appropriate increase in the penalty for those agents if they are caught cheating. This increase in penalty results in an increased divergence between the outcomes from being caught or not caught cheating for non-targeted agents, which increases the overall riskiness of the cheating option. And for risk-averse agents, this increase in riskiness is a disincentive to cheating. Recent research by Fraser (2004) shows numerically that quite modest increases in penalties for non-targeted agents caught cheating (i.e. 10-25 per cent) are suffi cient to deter agents with typical levels of risk aversion from such cheating.

Case studies: AEP and SFP

AEP in England in light of the Curry Report (Policy Commission) is being revised with the introduction of Environmental Stewardship (ES). ES builds on existing AEP in that it offers payments to farmers and other land managers who deliver effective environmental management on their land. ES has a number of main objectives including; maintaining and enhancing landscape quality and character; protecting the historic environment and natural resources; and promoting access by the public as well as an understanding of the countryside.

The ES is comprised of three elements:

1. Entry Level Stewardship (ELS)

2. Organic Entry Level Stewardship (OELS)

3. Higher Level Stewardship (HLS)

There is a strong

theoretical basis to support the adoption of greater use of target-based compliance monitoring as part of the revised and evolving CAP.




Let us focus on the ELS. It will be open to all land managers, with entry guaranteed providing scheme requirements are satisfi ed. By selecting certain environmental commitments land users earn points and once a points’ threshold is attained they gain payment (£30 per hectare per annum across the whole farm). ELS is designed to be simple in that little expert knowledge is required other than that held by a competent farmer. The main reason for this approach is that it is anticipated that the ELS will secure basic level environmental management.

In order to implement a system of targeting within the ELS it will be necessary to determine a number of clear geographical regions. Assume 10 for ease of illustration. Each region would have approximately the same number of participants. Then one of the regions would be designated for targeting in the fi rst monitoring period, and the remaining sub-groups not targeted. In subsequent monitoring periods the target sub-group would rotate around the 10 geographically defi ned subgroups. The exception to this would be participants caught cheating in previous monitoring periods who would remain targeted, at least for some number of periods.

The second step would be to adjust upwards the penalty associated with being caught cheating in order to increase the riskiness of cheating

and thereby exploit the risk aversion of participants in order to reduce or eliminate cheating among non-targeted participants. Finally, the effectiveness of the targeting system can be evaluated after several monitoring periods by comparing recent with historical levels of detected non-compliance.

Next consider the SFP, GAEC and cross-compliance. As designed in England breaches of cross-compliance will be subject to penalties that are proportionate to the severity, extent, permanence and repetition of non-compliance by the farmer (DEFRA, 2004). There are two types non-

compliance. Negligence incurs a percentage reduction in SFP of not more than fi ve per cent or in cases of repeated non-compliance up to 15 per cent. Intentional non-compliance will be subject to penalties of between 15 to 100 per cent of the SFP or in extreme cases exclusion from government programs for one or more calendar year.

As currently designed, GAEC is based upon standards that apply to soil management, the maintenance of habitats and landscape features. The standards are based in terms of desired outcomes broadly defi ned and are applied to all agricultural land. Targeting could be based upon adherence to a specifi c GAEC such as Waterlogged Soil (GAEC 3) or Heather and Grass Burning (GAEC 10). There is also nothing preventing government from implementing a target-based monitoring approach for designated agricultural/biophysical regions.

In the case of cross-compliance and GAEC a regional target-based approach may well be a sensible compliance-monitoring strategy. Because both policies focus on desired output a compliance-monitoring regime will need to be able to control for unobserved heterogeneity (e.g., weather). This type of heterogeneity will be regional. If compliance-monitoring decisions are not conditioned on this information, the identifi cation of inappropriate actions will be more diffi cult. Indeed it is easy to envisage situations where farmers would be penalised for not achieving the desired outcomes despite undertaking all the necessary and appropriate management actions.

Numerical Example of Mean-Penalty-Preserving Targeting

Consider a numerical example of an agent choosing whether or not to cheat a principal by failing to undertake an action for which payment is made by the principal.

Using a two-period setting, the agent’s base income is 20 in each period, while payment made by the principal is 18, and the action costs 10. Without targeting the probability of being caught cheating is 50 per cent and the penalty is 18, so the mean or expected value of the penalty is 9. With targeting, for an agent placed in the non-target group the probability of detection falls to 40 per cent, but the associated penalty is increased to 22.5 in order to provide a ‘mean-penalty-preserving’ (i.e. = nine) increase in the riskiness of the cheating option.

Using a standard constant relative risk aversion utility function of income with a risk aversion coeffi cient of 0.5, the following are the present values of expected utility:

Cheating Not cheating

Without targeting 21.41 21.17

With targeting 20.38 21.17

This example shows that without targeting the expected utility of cheating exceeds that of not cheating. But with the introduction of targeting and a ‘mean-penalty-preserving’ increase in the riskiness of cheating, the expected utility of not cheating now exceeds that of cheating. For further details of this example and a sensitivity analysis of parameter values see Fraser (2004).




Targeting and understanding non-compliance

Apart from the informational benefi ts that targeting will yield in terms of identifying non-compliance there are other reasons to take this approach. By targeting monitoring efforts policy makers may well be better able to understand if non-compliance is because of poor scheme design, or maybe lack of information regarding appropriate land management activities. In this way targeting will provide insights into the reasons for non-compliance, which may well be for reasons other than those associated with cheating. For example, it is widely recognised in the literature that motivation for compliance can be hindered by ignorance and/or the capacity to comply on the part of participants (Winter and May, 2001).

More effi cient and effective policy

There is a strong theoretical basis to support the adoption of greater use of target-based compliance monitoring as part of the revised and evolving CAP. The ability to identify groups of scheme participants and to focus monitoring efforts in a transparent and predictable manner would appear to require minimal effort on the part of policy makers.

Apart from the gains in more effective compliance monitoring the use of a targeted approach may well yield additional information to policy makers that can be used to inform and modify subsequent policy designs.

Finally, although we have not touched upon it in our analysis it is recognised in the literature that effective compliance monitoring and policy implementation not only requires monitoring but it is essential that breaches of the law are adequately and suitably punished. From the limited amount of information in the public domain it is clear that EU member states have enforced legislation in a patchy manner especially with respect to agriculture (CEC, 2004). There is clearly room for a signifi cant improvement in compliance monitoring and enforcement activity and it is hoped that targeting can help to achieve this.

Further Reading■ Australian Taxation Offi ce (2005). Compliance Program 2004-05, Australian Government. Downloaded on 7th July 2005. Available from: www.ato.gov.au

■ Choe, C. and Fraser, I. (1999). Compliance monitoring and agri-environmental policy, Journal of Agricultural Economics, 50 (3): 468-87.

■ Commission of the European Communities (2004). Fifth Annual Survey on the Implementation and Enforcement of Community Environmental Law. Commission Staff Working Paper, SEC (2004) 1025, Brussels.

■ Commission of the European Communities (2005). 2004 Environmental Policy Review. Communication from the Commission to the Council and the European Parliament SEC(2005) 97, Brussels.

■ DEFRA (2004). Single Payment Scheme. Cross Compliance handbook

for England. 2005 Edition, DEFRA Publications.

■ DEFRA (2005). Environmental Stewardship: Look After Your Land and Be Rewarded, Rural Development Service, DEFRA Publications.

■ Eckert, H. (2004). Inspections, Warnings, and Compliance: The Case of Petroleum Storage Regulation, Journal of Environmental Economics and

Management, 47: 232-259.

■ Fraser R.W. (2004). On the use of targeting to reduce moral hazard in agri-environmental schemes, Journal of Agricultural Economics 55 (3): 525-540.

■ Heyes, A.G. (2000). Implementing environmental regulation: enforcement and compliance. Journal of Regulatory Economics 17(2): 107-29.

■ Kirkham, F.W., Gardner, S.M., Critchley, C.N.R. and Mole, A. (2004). Using GAEC Cross-Compliance to protect Semi-Natural Habitats on

farmland and Unused Agricultural Land. A discussion paper prepared for Land Use Policy Group. Available from: www.lupg.org.uk.

■ Winter, S.C. and May, P.J. (2001). Motivation for Compliance with Environmental Regulations, Journal of Policy Analysis and Management, 20(4): 675-698.

Iain Fraser and Rob Fraser, Applied Economics and Business Management, Imperial College Wye Campus, UKEmail: [email protected]; [email protected]



summary

summary


From its inception the operation of the CAP has required the compliance

monitoring of farming activities and anticipated increases in the complexity of the monitoring tasks with the introduction of the SFP will stretch monitoring resources even further. We outline the scope for using targeting to enhance effectiveness of monitoring resources in dealing with non-compliant behaviour. Targeting resources can increase information on particular groups making identifi cation of inappropriate behaviour easier. However, in reallocating monitoring resources towards the targeted sub-group, there is likely to be an associated reduction in resources allocated to monitoring non-targeted agents and therefore an increase in their incentive to cheat. To reduce this problem we suggest balancing the targeting-induced reduction in the probability of being caught cheating for non-targeted agents with an appropriate increase in the penalty for those agents if they are caught cheating. This increase in penalty results in an increased divergence between the outcomes from being caught or not caught cheating for non-targeted agents, which increases the overall perceived risk of the cheating option. And for risk-averse agents, this increase is a disincentive to cheating. On this basis we argue for greater use of target-based compliance monitoring as part of the revised and evolving CAP.

Die Zielausrichtung von Überwachungsressourcen zur Effektivitäts-verbesserung der GAP

Targeting Monitoring Resources to Enhance the Effectiveness of the CAP

Si, dès l’origine, il a fallu contrôler la conformité des activités agricoles

avec les exigences de la PAC, il faut maintenant envisager que l’accroissement de la complexité des tâches de contrôle associées à l’introduction du paiement unique vont encore augmenter la pression sur les ressources disponibles en la matière. On examine ici dans quelle mesure il est possible de recourir au ciblage pour améliorer l’effi cacité des ressources affectées au contrôle des comportements incompatibles avec la PAC. Cibler les ressources permet d’accroître l’information sur certains groupes, rendant par là plus facile l’identifi cation des comportements illicites. Cependant, en réaffectant les ressources au contrôle prioritaire des sous-groupes à risque, on va probablement diminuer celles qui concernent les agents non prioritaires, et par conséquent augmenter l’incitation à tricher pour ces derniers. Pour surmonter cette diffi culté, il est suggéré de contrebalancer l’effet de la réduction de la probabilité d’être pris en train de tricher par une augmentation des pénalités imposées en ce cas. Un tel accroissement des pénalités conduirait à augmenter l’écart entre les situations « pris » ou « pas pris », et donc la perception du risque associé à l’option « tricher » chez les agents des groupes qui ne font pas l’objet du ciblage. Et chez des individus dotés d’aversion pour le risque, une telle situation est de nature à diminuer l’incitation à tricher. Cet article milite donc sur cette base en faveur d’une utilisation accrue des méthodes de contrôle ciblé qui devraient faire partie intégrante des évolutions à venir de la PAC.

Die Anwendung der GAP machte es von Anfang an erforderlich,

die Einhaltung ihrer Bestimmungen auf Betriebsebene zu überwachen. Die bei der Einführung der Betriebsprämienregelung zu erwartende Zunahme an Komplexität der Überwachungsaufgaben wird die Überwachungsressourcen noch weiter ausdehnen. Wir zeigen Möglichkeiten auf, wie durch gezielte Maßnahmen eine erhöhte Effektivität von Ressourcen bei der Überwachung von regelwidrigem Verhalten erreicht werden könnte. Gezielte Maßnahmen führen zu besseren Informationen über bestimmte Gruppen und erleichtern die Identifi kation von regelwidrigem Verhalten. Durch die Konzentration der Kontrolle auf gezielte Untergruppen wird jedoch der Kontrollaufwand für andere Gruppen reduziert; die Folge wird sein, dass sich hier der Anreiz zum Betrug erhöht. Zur Verminderung dieses Problems empfehlen wir, die verringerte Wahrscheinlichkeit, beim Betrug ertappt zu werden, mit einer angemessenen Erhöhung des Strafmaßes auszugleichen. Das höhere Strafmaß erhöht die Opportunitätskosten des Regelverstoßes und verringert daher den Anreiz zum Regelverstoß. Auf Grund dieser Überlegungen treten wir für die verstärkte Anwendung einer zielgerichteten Überwachung zur Einhaltung von Bestimmungen als Teil der überarbeiteten und sich entwickelnden GAP ein.

Cibler le contrôle des ressources pour améliorer l’efficacité de la PAC



targeting monitoring resources to enhance the effectiveness of the cap

Documents