Summary of the Sevilla EFIMAS/COMMIT Economists meeting

1. Summary of the Sevilla EFIMAS/COMMIT Economists meeting Simon Mardle (and Sean Pascoe) Centre for the Economics and Management of Aquatic Resources (CEMARE), University of Portsmouth, Boathouse 6, College Road, H.M. Naval Base, Portsmouth, Hants., PO1 3LJ, UK

2. Agenda • Overview of dynamics in existing bioeconomic models • MEPHISTO (GEM) • MOSES/IBEM (IREPA) • TEMAS (DIFRES) • EIAA (FOI) • Fleet dynamics (what do we want to capture?) • Effort levels and allocations? • Capacity utilisation? • Investment? • Entry/exit? • Price dynamics • Cost dynamics • Other economic modelling issues

3. Fleet/Price/Cost dynamics • Critical appraisal of the suitability of existing approaches to EFIMAS/COMMIT models • constant prices, flexibility based, demand curves etc? • which costs do we want to consider and how are these evaluated? • What alternative approaches may exist? • how dynamic do we want prices to be and what are the main drivers of prices in the different case study fisheries (exogenous or endogenous). • which costs should change (e.g. fuel?) or do we assume that they remain constant in real terms? • Which models are best suited to which case studies? • do we develop a portfolio of models? • Data requirements of the different approaches? • Specification of key model equations to use in the models.

4. Current models • Aimed at management of multi-species, multi-fleet, multi-gear fisheries • Northern Europe and Mediterranean • Dynamics? • Fleets • Prices • Costs • Lots of knowledge to build on

5. MEFISTO (Bio-Economic simulation model for the Mediterranean Fisheries) • - Multi-species analytic model • Accessory species • Multiple fleets & multiple gears

6. MEFISTO (Stock Box) • Main species  Analytic model: • growth model (length/weight; von Bertalanffy) • Vector of individuals by age class • Vector of Natural mortality; Fecundity index • Reproduction and recruitment functions • Accessory species yield income as a function of main species catch. Multiplicative or additive approaches: Y = a Cb Y = a + b C

7. MEFISTO (Fisherman decision process) q increase Costs increase q decrease Effort decrease

8. MOSES (integrated bio-economic model with special emphasis on the management of resources and social impacts of fisheries) • Methodology • bio-economic modelling • classical and Bayesian statistical estimation techniques • extensive computer programming • numerical calculation • linear and non-linear optimisation techniques. • Model characteristics • support to public administration management policy • optimal allocation of fishing effort by gear and area • Biological parameters are endogenous Logistic: 1) Schaefer, 2)Exponential - Dynamic Age-structured: 3) Schnute/Deriso

9. Moses (Model structure)

10. TEMAS in one slide • A Bioeconomic model (cf. BEAM 5) • Based on “standard biologic thinking” –i.e. cohort model with growth, recruitment, migration, M+F, gear selection etc. (equivalent to an ICES forecast). • Multi-fleet, Multi-fishery, Multi-species, Multi-area. • Includes management and economics • Fleet-based framework used • Dimensions: fleets, fisheries, stocks

11. TEMAS (Current work) • Development of a “Fleet behavior” module • Based on empiric analysis (RUM) • Including economics and ‘tradition’ • Results in the fleets effort allocations between different ‘fisheries’ • Simultaneous comparison of two alternatives scenarios (two modules ): • Operating model : “true world” • Management procedure : sampling, ICES WG+ACFM, management • Effort constrained by management

12. EIAA (Model Structure) Projected variable costs Projected economic results Change in fleet activity Projected segment revenue Projected prices Up-take ratio Stock-catch flexibility Segment catch of quota species National share Segment catch base year SSB National quota TAC (EU) Price flexibility Model prices base year Price flexibility Value other species Variable costs base year National catch base year Crew share base year Segment share Crew share Fixed costs base year Crew share

13. Dynamic state variable models in fleet dynamics • Multi-species multi-patch model of decisions in Dutch beam trawl fisheries (in prep.) • ITQs two species (Sole and Plaice) • Several patches with different seasonal catch rates • Optional high-grading • When are changes in fishing behaviour under catch quota expected? • Dynamic • Calculation consists of two stages: • Backward, calculating “value” of each state, based on probability * Utility, resulting in optimal choice for each state at each timestep • Forward, based on optimal choice and resulting catch rates (stochastic), eg. Markov chain • The expected revenue function used for backward calc and and actual revenues in forward calc may differ

14. Exit/entry • “Enter” - vessels that have joined • “Exit” - vessels that have left fleet • “Stay” - vessels that stay • “Change port” • “Change licence”… “owner” … “country” … • A priori… • Vessels that enter - higher catch rate • Vessels that exit - lower catch rates(VPUE)(~low profit)

15. Exit/Entry (English North Sea beamers)

16. Exit/Entry state-of-the-art (RUM) • Pradhan and Leung (Marine Policy, 2004) • relative revenue (per GT) • fleet congestion • stock conditions • residency, vessel age, captainship • Strategy / Tactics Survey – Links • Explanatory variable links -> entry prediction

17. Fleet dynamics

18. Results - Fleet dynamics • Two types: • Spatial/temporal allocation of effort • RUM • Optimisation • Simple rules • Entry exit • Net exit model (assume no new entrants) • Investment models (being looked at in CAFÉ)

19. Price dynamics

20. Price Dutch auctions

21. MEFISTO (Market Box) So, Total Revenuesare calculated: TR = ∑(Ps · Cs + Y) Price function per fleet (for each main species): P = b0 * size^b1 * local-offer^b2 * b6 + ε Which can be transformed to a logarithm model: Ln (Price) = ln (b0) + b1 · ln (size) + b2 · ln (local-offer) + ln (b6) + ε (External factors are included)

22. TEMAS (Prices) • Ex. Vessel prices • Maximum price, Pmax (over age groups) • Price of age group “a” relative to Pmax • Prices are given as input : either assumed to remain constant or to vary as a result of changes in supply (i.e. in landings) • where Pflex(Fl,St), is the price flexibility • The introduction of “cross-price flexibilities” are stipulated in the manual

23. Price module • Robust model that can handle: • extensive changes of landings/TAC’s • short run and long run impact • Aim: consistent set of prices for different species • Possible solutions: • Aggregated demand curves including all competing species • same price elasticity for own supply and supply of competing species • Fixed ratio between prices of different species P= Po*(Ldem_t / Ldem_t-1)^f • Different short term and long term elasticities • Long term: fixed prices? P= Po * (Lt/Lt-1)^f

24. Price dynamics

25. Results - Price dynamics • Constant price (simple approach if no time series of data) • Long-run average price • Monthly Vs Yearly prices (i.e. can add a seasonal component) • Can factor in trends in world prices (exogenous to the model) • Variable prices • Demand models by species (too many to deal with) • Unlikely that can adequately incorporate cross-price effects • Simple Catch/Price flexibility relationship for groups of species • Similar to EIAA approach and other models (e.g. DEMINT North Sea model) • Use cointegration analysis to identify species who’s prices move together • And relationship between prices • Still need to estimate group price flexibility • Less complex than for individual species

26. Cost dynamics

27. TEMAS (Economic sub-model) • FINANCIAL ANALYSIS OF FLEET: • the financial performance of fishing fleets (i.e. from the point of view of vessel owners). • GOVERNMENT BUDGET ANALYSIS: • The impact of the fleets on the government budget. • ECONOMIC ANALYSIS: • The economic performance of fishing fleets and the entire fishery.

28. TEMAS (The “firm” account)

29. MEFISTO (Costs)

30. Cost dynamics

31. Results - Cost dynamics • Variable costs • Change with effort (fuel+running, repair, maintenance) • Change with catch (crew, commissions, levies) • Fixed costs • Cash (crew (some case studies), repair, maintenance, admin) • Non cash • use agreed economic depreciation rates, skipper shares • Opportunity cost of capital (rather than interest payments) • Capital value • Boat • Licence (need to vary with profit levels) Note: one consistent allocation per case study!

32. Where are we? • Minutes of Sevilla meeting (to be completed) • Leading to a more complete synthesis of the details (i.e. functional forms etc) • Case study development • FLR: • Fleet Object? • Logistics wrt control process (e.g. effort optimisation etc)