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. 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

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. 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

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 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)

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. 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

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)