AGNELLO: ECONOMIC VALUE OF FISHING SUCCESS 



Q and P is embodied in the slope of the curve. Rela- 

 tionships between Q and income (I) as well as other 

 variables (S) can be shown as shifts in the demand 

 curve. For simplicity and without loss of general- 

 ity, let S represent single fishing success variable. 

 For example, an increase in a relevant variable such 

 as fishing success (S) from S, to So is shown as a 

 shift in the demand curve from Dj to D, (i.e., from 

 f(P, I, S,) to f(P, I, S2). 



The value of an improvement in site quality, such 

 as an increase in fishing success, can be measured 

 in various ways. A common approach is to compare 

 the areas under each demand curve and evaluate an 

 increase in fishing success as a difference in the area 

 over some quantity range (Freeman 1979). For ex- 

 ample, let us assume that in a particular year or 

 season an individual consumes Qi units at a price 

 of Pi when the level of fishing success is Sj (i.e., 

 reflected by demand curve Dj). Suppose the level 

 of fishing success increases to S, (e.g., during the 

 next year or season). Given the demand shift to Do 

 and the old price of Pj, the individual would now 

 consume Q2. The economic valuation for the im- 

 provement in success or site quality totaled for the 

 fishing season or year is approximately measured 

 as the sum of areas A, B, and C. These areas repre- 

 sent an increase in consumer surplus for the fisher- 

 man experiencing an increase in fishing success and, 

 thereby, increasing the fishing level from Qi to Qo. 



An alternative approach to valuation of fishing 

 success is to measure the instantaneous (or mar- 

 ginal) change in welfare when fishing success 

 changes (i.e., on a per-visit basis) rather than the 

 accumulated gain over an entire season. This is the 

 primary focus of our paper and can be accomplished 

 in various ways. One approach is to convert the con- 

 sumer surplus over an entire season into that of a 

 single trip by dividing areas (A, B, C) by the num- 

 ber of trips per season. A more direct approach can 

 be accomplished by first solving Equation (1) for P. 

 For the moment, let us assume that Equation (1) 

 is deterministic (i.e., nonstochastic) in nature and 

 can be inverted mathematically. Thus we can solve 

 for P as 



P = g(Q, I, S). 



(2) 



Equation (2) is often referred to as the inverse de- 

 mand function. The marginal value of fishing suc- 

 cess a P/3 S can be measured as ag/9 S from Equa- 

 tion (2) where did represents the partial derivative 

 operator. In Figure 1, this may be viewed as the 

 distance (P2 - Pi) when the number of fishing trips 

 is Qi. This second approach will be the primary 



focus of the empirical analysis. It is more direct, has 

 the advantage of less extrapolation from typical 

 values of P and Q, and avoids any potential difficul- 

 ty with an unbounded measure for area A arising 

 with certain functional forms. 



DATA 



Since fishing trips and success are not commodi- 

 ties bought and sold in the marketplace, data are 

 not readily available on P, Q, and S. As a result, 

 survey methods are usually used to generate data 

 on the number (Q) and price (P) of fishing trips and 

 fishing success (S). The two most common survey 

 approaches for relating Q, P, and S for individual 

 fishermen have been 1) to directly ask marine 

 anglers for valuation estimates of hypothetical 

 changes in fishing trip frequency and success, or 

 2) to impute implicit valuation or trade-offs based 

 on the various cost and activity level responses of 

 a cross section of marine anglers. The first approach 

 is usually referred to as contingent valuation and 

 has been employed in fisheries valuation. Recent 

 studies using contingent valuation surveys which 

 attempt to incorporate catch rate and site informa- 

 tion include Cameron and Huppert (in press) and 

 Cameron and James (1987). The second approach 

 using the travel cost method focusing on individual 

 marine anglers will be used in this study. The travel 

 cost method, although not without pitfalls, has been 

 widely accepted as a means for valuing recreational 

 resources when distance for fishing trips is well 

 defined. An early implementation of the travel cost 

 method can be found in Clawson (1959). For a re- 

 cent summary of the travel cost method and its com- 

 plexities, see Kealy and Bishop (1986). 



The individual travel cost approach to evaluation 

 relates travel cost and visitation frequency to rec- 

 reational sites for individuals. This relationship pro- 

 vides an indirect way of observing how individual 

 visitation frequency might respond to changes in an 

 entry or purchase price as in a traditional economic 

 demand relationship. Thus, behavior of marine ang- 

 lers with respect to travel cost, travel frequency, 

 and site quality (e.g., fishing success) provides the 

 basis for estimating a demand equation for marine 

 recreational fishing. The parameters of Equation (1) 

 and/or (2) can be estimated using cross-section data 

 on individual anglers. 



In this study we are able to measure travel cost, 

 travel frequency, success, and income variation for 

 individuals from the Socioeconomic Survey con- 

 ducted as a part of the Marine Recreational Fishery 

 Statistics Survey (MRFSS) by the National Marine 



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