COBECOS Fisheries Enforcement Theory: Basic Elements A Presentation at the Special Workshop for the EU Commission and Fisheries Control Administrations.

Slides:



Advertisements
Similar presentations
Fisheries Enforcement Theory Contributions of WP-3 A summary Ragnar Arnason COBECOS Project meeting 2 London September
Advertisements

COBECOS Case study on Icelandic cod. Overview Common types of violations Modeling approach –Using COBECOS code –Using our own code Results Conclusions.
COBECOS mid-term meeting, 2-3 September 2008, San Sebastian Italian case study: GSA 9 bottom trawling fishery Status of model estimation at September 2008.
Brussels the 3rd of December COBECOS 1 COMPUTER MODEL Costs and Benefits of Control Strategies SIXTH FRAMEWORK PROGRAMME Policy-Oriented Research.
05/04/2011 Public Hearing Added value of collective redress for improving the enforcement of EU law: entering a new debate Jérôme P. Chauvin Director Legal.
COBECOS Costs and Benefits of Control Strategies WP-8 SIMULATIONS.
WP 6 progress Cefas, Imperial (IC), AZTI, JRC (all participants) San Seb – Sep 2008.
COBECOS Costs and Benefits of Control Strategies WP-8 SIMULATION.
TILEC – T ILBURG L AW AND E CONOMICS C ENTER Convergence and Divergence in Competition Law Filomena Chirico Norwich, 12 June 2008.
Research Proposal PIDE and Iran. Prudent economic management is essential for putting the economies on the path of sustainable economic growth. Over the.
Intra-Industry Trade, Imperfect Competition, Trade Integration and Invasive Species Risk Anh T. Tu and John Beghin Iowa State University.
Simulating Single server queuing models. Consider the following sequence of activities that each customer undergoes: 1.Customer arrives 2.Customer waits.
Project Analysis and Evaluation
Engineering Economic Analysis Canadian Edition
STCPM title A model of bank price and nonprice competition with endogenous expected loan losses Filipa Lima Paulo Soares de Pinho Emerging Scholars in.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
Sustainable Economics in the Management of Fisheries and Natural Resources in the North Atlantic Nordic conference on the protection of the sea and living.
Managing Variability: Rules, Incentives, and Behavior Mike Dalton California State University Monterey Bay Marine Biodiversity: Using the Past to Inform.
458 Population Projections (policy analysis) Fish 458; Lecture 21.
I. What I do Economic, statistical, and mathematical models relating to environmental management. Dynamic allocation of resources. Decision-making under.
Spatial fisheries management in practice: an example.
“Fishing” Actually, coupled Flow, Fish and Fishing.
1 Stochastic Modeling for Clinical Scheduling by Ji Lin Reference: Muthuraman, K., and Lawley, M. A Stochastic Overbooking Model for Outpatient Clinical.
Economics of Biotic Resources Ecosystem Structure and Function.
Minimum wage compliance in Latin America The weight of economic and institutional factors Andrés Marinakis ILO – Santiago
AGEC/FNR 406 LECTURE 7 An irrigated rice field in Bangladesh.
Opportunity Engineering Harry Larsen The Boeing Company SCEA 2000 Conference.
Modeling and Simulation
Fisheries Enforcement: Basic Theory Paper presented at COBECOS Kick-off meeting Salerno February, 22-3, 2007 Ragnar Arnason.
Techniques for selecting projects
Economics of Alternative Energy Sources and Globalization Nov Orlando, Florida The Economic Feasibility of Bio-energy Generation for Peak Demand.
Introduction to Simulation Modelling Chapter1. Models as convenient worlds  Our lives, as individuals or families or workers in an organization, are.
Enforcement & Compliance in Pollution Permit Markets Jim Murphy John Stranlund This research is funded by USEPA STAR Program grant #R
Ragnar Arnason Markets Promote Maximum Possible Liberty European students for Liberty Reykjavik, October
Ussif Rashid Sumaila ( Jackie Alder Heather Keith Fisheries Economics Research Unit Sea Around Us.
AMERICA’S ARMY: THE STRENGTH OF THE NATION Mort Anvari 1 Cost Risk and Uncertainty Analysis MORS Special Meeting | September.
UNIT 10: Monitoring and Compliance. 2 Monitoring & compliance Activity 10.1: Class prior experience and local examples of compliance and monitoring activities.
Unit 3 Fisheries Enforcement: major aspects Coastal Fisheries Policy and Planning Course, 28/01/08 – 8/02/08 Apia, Samoa Peter Manning - FAO Secretariat.
Market power1 ECON 4925 Autumn 2006 Resource Economics Market power Lecturer: Finn R. Førsund.
Income Benchmark Applied Inclusive Growth Analytics Course June 29, 2009 Leonardo Garrido.
1 UST Stakeholders Meeting Compliance & Enforcement “C/E 101” MassDEP January 2012.
DEEPFISHMAN Using bioeconomic modeling for evaluation of management measures – an example Institute of Economic Studies.
Enforcement of the Icelandic cod fishery – A two management control, two enforcement tool fishery – Ragnar Arnason Preliminary results from the Cobecos.
A Theoretical Framework for Adaptive Collection Designs Jean-François Beaumont, Statistics Canada David Haziza, Université de Montréal International Total.
Re-use of the Public Sector Information: Demand and Implementation Guoda Steponavičienė, Lithuanian Free Market Institute,
CHAPTER TWO NOTES AP I.FUNDAMENTAL FACTS OF ECONOMICS A. UNLIMITED WANTS 1. ECONOMIC WANTS ARE DESIRES OF PEOPLE TO USE GOODS AND SERVICES THAT PROVIDE.
Economics of Biotic Resources Ecosystem Structure and Function.
Unilateral Exclusionary Conduct – An Analytical Framework Jorge Fagundes 3rd Coloquio - ForoCompetencia Buenos Aires, Argentina – November 2, 2007 Fagundes.
1 Economics 331b Treatment of Uncertainty in Economics (I)
Chapter – 1 Nature and Scope of Cost Accounting
Presented to Managers. INTERNAL CONTROLS are the integration of the activities, plans, attitudes, policies and efforts of the people of an organization.
Modeling Liquidity and Income in Modern Portfolios Todd E Petzel, CIO Offit Capital Advisors QWAFAFEW New York March 24, 2009.
Decision Theoretic Planning. Decisions Under Uncertainty  Some areas of AI (e.g., planning) focus on decision making in domains where the environment.
Evaluation of harvest control rules (HCRs): simple vs. complex strategies Dorothy Housholder Harvest Control Rules Workshop Bergen, Norway September 14,
Optimal continuous natural resource extraction with increasing risk in prices and stock dynamics Professor Dr Peter Lohmander
Capital Adequacy and Allocation John M. Mulvey Princeton University Michael J. Belfatti & Chris K. Madsen American Re-Insurance Company June 8th, 1999.
Introduction to Economics. Outline I. What is Economics A. Formal Definition B. Informal Definition.
1 Regulation as a policy contest: The effect of changed environmental conditions on the probability of conservation of a renewable resource Urs Steiner.
Enforcement of the Icelandic cod fishery – A two management control, two enforcement tool fishery – Ragnar Arnason Icelandic Case study for the COBECOS.
1 COBECOS – Lone Grønbæk Kronbak Cost and Benefits of Control Strategies (COBECOS) - the case of Norway lobster trawl fishery in Kattegat and Skagerrak.
Economics of Overexploitation Revisited Presented by R. Quentin Grafton Co-authors Tom Kompas and.
UNECE – SC2 Rail Security Analysis and economic assessment of rail transport security 1st October 2009 Andrew Cook.
1 Tom Edgar’s Contribution to Model Reduction as an introduction to Global Sensitivity Analysis Procedure Accounting for Effect of Available Experimental.
NMA course Marko Lindroos
QMT 3033 ECONOMETRICS QMT 3033 ECONOMETRIC.
Mixed fisheries issues for North Sea Cod
Prepared by Lloyd R. Jaisingh
Chapter 8: Selecting an appropriate price level
Linear Programming Topics General optimization model
Introduction to Economics
Presentation transcript:

COBECOS Fisheries Enforcement Theory: Basic Elements A Presentation at the Special Workshop for the EU Commission and Fisheries Control Administrations Ragnar Arnason Bruxelles, December 3, 2008

Introduction Fisheries management needs enforcement –Without it there is no fisheries management Enforcement is expensive Enforcement is complicated Optimal fisheries policy needs to take enforcement into account Enforcement theory is fundamentally the theory of crime (Becker 1968)

Model: Key Elements Social benefits of fishing: B(q,x)+ ·(G(x)-q) Shadow value of biomass Enforcement sector: Announced target:q* Enforcement effort:e Cost of enforcement:C(e) Probability of penalty: (e) Penalty function:f(q-q*) Private benefits of fishing:B(q,x)

Model (cont.) Probability of penalty function: (e) (e) e 1

Model (cont.) Penalty function: f(q-q*) f(q-q*) q q*q* Corner

Model (cont.) Private benefits under enforcement Social benefits with costly enforcement: B(q,x)- (e) f(q-q*) B(q,x)+ (G(x)-q)-C(e)

Private behaviour Maximization problem: Max B(q,x)- (e) f(q-q*) Enforcement response function: q=Q(e,x,q*) Necessary condition: B q (q,x)- (e) f q (q-q*)=0 Key relationship!

Private maximization $ q q* Marginal benefits of fishing, B q Marginal penalty costs, (e) f q q enf q°

q e q* [lower f] [higher f] Free access q Enforcement response function

Optimal enforcement Social optimality problem B(q,x)+ (G(x)-q)-C(e). subject to: q=Q(e,x,q*), e 0, q* & penalty structure fixed. Necessary condition:

Optimal enforcement $ q B q - q cost BqBq q° q* C e /Q e =C q q costless

To apply theory: Empirical requirements 1.The private benefit function of fishing, B(q,x) 2.The shadow value of biomass, 3.The enforcement cost function, C(e) 4.The penalty function, (e) 5.The penalty structure, f(q-q*) Note: Items 1 & 2 come out of the usual bio- economic model of the fishery. Items 3, 4 and 5 are specific to enforcement

Extensions 1.Higher dimensions –Many fisheries actions –Discrete fisheries actions –Many enforcement tools 2.Enforcement under uncertainty 3.Enforcement when avoidance is possible 4.Optimal fisheries dynamic paths with costly enforcement

Higher dimensions N fisheries actions s=(1xN) vector M enforcement tools e=(1xM) vector (e) =(1xN) vector f(s-s*) =(1xN) vector Fishers: Select profit maximizing vector s Enforcers: Select benefit maximizing vector e More complicated, but essentially the same!

Enforcement under uncertainty All components of enforcement model are subject to uncertainty This can have an impact on best enforcement Must take account of this Some theoretical investigations Usually enforce more (to reduce risk) In practice: Monte Carlo simulations

Enforcement under uncertainty Example Compare (1) maximization of benefits ignoring stochasticity to (2) maximization of expected benefits (proper procedure)

Enforcement when avoidance is possible (e) (e,a) a is avoidance activity New social cost: C(a) Analysis becomes more complicated –compliance may be reduced when e or f increase!! The social benefits of enforcement are reduced, sometimes drastically

Optimal Fisheries Dynamics S.t. Essentials, if is also a control

Optimal Fisheries Dynamics with costly enforcement: An illustration C e >0 C e =0 Biomass, x Harvest, q

END

Discrete fisheries actions Some fisheries actions are either/or –E.g. either use dynamite or not, either enter a closed zone or not, etc. These are discrete actions Need to extend the theory to deal with that Straight-forward; But maximality conditions more complicated

The discontinuity problem Analytically merely cumbersome Practically troublesome –Stop getting responses to enforcement alterations To avoid the problem –Set q* low enough (lower than the real target) –Aim for the appropriate level of noncompliance A well chosen q* is not supposed to be reached ( Non-compliance is a good sign!)

Some observations 1.Costless enforcement traditional case (B q = ) 2.Costly enforcement i.The real target harvest has to be modified (....upwards, B q < ) ii. Optimal enforcement becomes crucial iii.The control variable is enforcement not harvest! iv.The announced target harvest is for show only v.Non-compliance is the desired outcome 3.Ignoring enforcement costs can be very costly i.Wrong target harvest ii.Inefficient enforcement

Model (cont.) q (q;e,f,q*) q* (e) f Private costs of violations: (q;e,f,q*)= (e) f (q-q*), if q q* (q;e,f,q*) = 0, if q<q*

Social optimality: Illustration e $ e*e* e°