Interpreting price elasticities and flexibilities of demand at internationally integrated seafood markets Max Nielsen Institute of Food and Resource Economics, University of Copenhagen Workshop in Nantes, 14-15 April 2011 ”Modelling Global Demand for tuna”

Outline Issue What determine the price of fish? Integration of fish markets Fish price formation Modeling fish demand Consequences for fisheries and management

1. Issue Focus: Raw material flow Product flow To overview fish price formation based on market integration and demand analysis. To discuss suitable demand models irt. Marshallian vs. Hicksian Ordinary vs. inverse Focus: Raw material flow Product flow Fishermen Contracts and auction market Processing Transport and storage Wholesale trade Retail trade and catering

2. What determine the price of fish? Price level determined by: Catch method and quality Size of fish Competition Eco-labeling Price development determined by Supply own/substitutes AC, MC, productivity Demand population, income, preference growth Exchange rates Market structure/integration decisive

3. Integration of fish markets A market is ”the area within which the price is determined” (Stigler 1969) Testing the Law of One Price (LOP) Stationary data , when A=1 LOP in force Non-stationary data (VAR model in ECM form) decomposed into with co-integrating vectors in A common integrating factor (n-1) is sought Given rank (n-1) LOP is a test of: Test results M. integration Demand modelling LOP accepted Perfect Aggregate variables Rank = (n-1) Partial Model var. with substitutes Rank < (n-1) None Exclude variables

Market integration internationally/inter-species Whitefish Selected results Market integration internationally/inter-species Whitefish Intern. integration in single species – relative perfect Between species – exist, not perfect Salmon Intern. integration in farmed salmon – perfect Intern. Integration farmed/wild pacific salmon – probably exist Integ. large trout/salmon perfect, small trout/salmon absent Herring Norwegian-Danish market integration – relative perfect Close market integration internationally - only between species in some cases

4. Fish price formation What determine the price of cod? Whitefish = cod, hake and haddock.

5. Modelling fish demand Market integration - pre-test of aggregation in demand analysis Choice of model Marshallian Hicksian demand Inverse ordinary demand Applying inverse demand models Dual approach

Marshallian and Hicksian demand Marshallian – double-log Hicksian – AIDS Restrictions: Adding up Homogeneity   Symmetry Choice Theoretical consistency AIDS - preference structure approximated around the optimal point for the true preference structure Double-log – arbitrary preference ordering Statistical fit - testability of model AIDS restrictions testable, many do not test if tested many reject but impose anyway Double-log not testable

Inverse and ordinary demand Inverse : p = f(q, m) Price flexibility = “%-change in price due to a 1% increase in quantity” Ordinary: q = f(p, I) Assumptions: p p p Supply Supply Supply q q q Inverse demand Ordinary demand “Real supply” Choice of model Inverse – landing markets and analyzing price formation Ordinary – retail and wholesale markets – aquaculture? Mixed demand systems Test inverse and ordinary for statistical fit

Empirical studies finds: Comparing Theory states: Empirical studies finds: Houck (1966)/Huang (1994) also find systematic difference Difference due to degree of substitution included: Ordinary – price exogenous, influenced by substitutes Inverse – quantity exogenous, only influenced by one fishery unreliable approximation for Average Inverse demand Ordinary demand - of 75 own price flexibilities -0.30 [-1.00; 0.00] . - of 66 own price elasticities (inverted) -0.94 [-4.76; -0.05]

Applying inverse demand models Price effects of a 10% reduction in quantity on the total market

European fish market integration (JAE 2009) Issue – identify total fish demand in Europe Assume perfect market integration for single species in Europe Identify market integration between 23 species fresh/frozen Estimate inverse demand –species with m. integration included Market integration test – VAR in ECM non-stationary price series Market integration: Data and estimation Monthly data 1995-2006 from EU-15, NO, ICE and FAR Co-integration for up to 4 species search procedure Potential substitutes clustered irt. type, color of meet, size price level and fat content Results – 20 reliable co-integrated models identified LOP holds in 7 models

Demand for fish in Europe (MRE resubmission) Model – double-log inverse co-integrated demand model estimated for non-stationary variables Own-price flexibilities for fresh fish Average – 1.10 Cod – 1.26 Sole – 1.17 Hake – 0.21 Plaice not ident., own quantity unimportant – many subst. Larger own-price flexibilities than for country-wise studies A larger share of global markets included Other reasons? Co-integrated demand?

6. Consequences for fisheries and management Depend on: Extent of transforming price changes through value chain The fisheries share of total world supply Small – fishery price taker, management does not affect prices Large – price is relevant in setting optimal fisheries management Quota changes can be coordinated Ex. a recovery plan Elastic demand lower long-run prices But: what about the transition period? p SOA=AC SOM=MC A B D q

Taking price effects into account might ease recovery plans Effect on turnover of changing quantities at internationally integrated markets – transition period for recovery plans Turnover = price*quantity Taking price effects into account might ease recovery plans