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

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

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.

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

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

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

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

Model characteristics 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

Moses (Model structure)

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

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

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

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

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)

Exit/Entry (English North Sea beamers)

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

Fleet dynamics

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)

Price dynamics

Price Dutch auctions

MEFISTO (Market Box) So, Total Revenues are 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)

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

P= Po*(Ldem_t / Ldem_t-1)^f 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

Price dynamics

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

Cost dynamics

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.

TEMAS (The “firm” account)

MEFISTO (Costs)

Cost dynamics

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!

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)