Abstract. -This study develops 

 and demonstrates a framework for 

 measuring changes in the total-fac- 

 tor productivity of fishing fleets op- 

 erating in common-property, open- 

 access fisheries. This approach is 

 distinguished from previous efforts 

 to measure productivity growth in 

 fisheries by our explicit treatment of 

 the fishery resource as a constraint 

 on production and by the incorpora- 

 tion of recent advances in produc- 

 tivity measurement that take into 

 account variations in the degree of 

 capacity utilization in an industry. 

 The approach is developed in suffi- 

 cient detail for the non-economist 

 fishery analyst to follow and imple- 

 ment. The empirical analysis of total- 

 factor productivity growth in the U.S. 

 tropical tuna fleet reveals that this 

 approach eliminates a significant 

 amount of bias in fleet productivity 

 measures which is otherwise intro- 

 duced when using traditional methods 

 of productivity analysis. 



On Measuring Fishing Fleet 

 Productivity: Development 

 and Demonstration of an 

 Analytical Framework 



Samuel F. Herrick, Jr. 

 Dale Squires 



Southwest Fisheries Center, National Marine Fisheries Service, NOAA 

 PO. Box 271, La Jolla, California 92038 



Maiiuscri])t accepted 31 August 1989. 

 Fishery Bulletin, U.S. 88:85-94. 



Measuring changes in productivity 

 has long been an important compo- 

 nent in evaluating an industry's eco- 

 nomic performance. Such measures 

 in fishing industries can signal the 

 need for, as well as indicate the suc- 

 cess of, policy or management ac- 

 tions. Measurement of productivity 

 growth or technical progress in ma- 

 rine fishing industries has received 

 attention by Bell and Kinoshita 

 (1973), Davis et al. (1987), Duncan 

 (n.d.), Kirkley (1984), and Norton 

 et al. (1985). 



Two considerations not addressed 

 by previous researchers are impor- 

 tant for evaluating productivity gi-owth 

 in fishing industries. First, tradition- 

 al measures of productivity implicit- 

 ly assume that a fishing industry's 

 capital stock is being utilized, in an 

 economic sense, at its long-run ecjui- 

 librium or capacity output level. Thus, 

 traditional measures fail to adjust for 

 variations in the degree of utilization 

 of the industry's productive capacity. 

 Second, the effect of changes in 

 abundance of the fish stock on pro- 

 ductivity growth in fishing industries 

 has not been specifically accounted 

 for in the traditional analysis. As a 

 result, changes in resource abun- 

 dance are not disentangled from 

 changes in productivity. 



In this study we develop a non- 

 parametric framework utilizing the 

 method of growth accounting and 

 economic index numbers to analyze 

 the productivity growth of fishing 



fleets operating in common-property, 

 open-access fisheries. The framework 

 is then demonstrated by analyzing 

 productivity growth in the U.S. trop- 

 ical tuna purse seine fleet over the 

 years 1981-1985.^ We also demon- 

 strate a method for deriving implicit 

 aggregate output and input price in- 

 dices for the purse seine fleet. 



In developing the productivity as- 

 sessment framework, we introduce 

 further refinements to the standard 

 procedure described by Denny et al. 

 (1981) and Cowing et al. (1981)- 

 which has seen continual improve- 

 ment since the pioneering work of 

 Solow (1957)— by adjusting for vari- 

 ations in capacity utilization and 

 changes in resource abundance. The 

 exposition includes the technical 

 detail necessary to provide the non- 

 economist fishery analyst with a com- 

 prehensible and useful means of 

 tracking and analyzing productivity 

 gi'owth and performance in fisheries. 



'The method is non-parametric because param- 

 eters of the production technology or produc- 

 tion function are not econometrically esti- 

 mated. The method of growth accounting and 

 economic inde.x numliers is discussed in a later 

 section of the te.xt. Econometric estimation 

 of productivity growth does not impose the 

 conditions of a constant-return.s-to-scale pro- 

 duction technology and Hicks-neutral tech- 

 nical change. However, this comes at the ex- 

 pen.se of more demanding data requirements: 

 either a longer time-series of aggregate data 

 or more vessel-level observations in any given 

 year. In addition, some fairly sophisticated 

 econometrics and economics are required to 

 estimate and interpret the results. 



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