Public Economics, Research Methods/ Statistical Methods,

Anderson, James L.

Parsons, George R.

English

Research Papers in Economics

99
Marine Resource Economics, Volume 24, pp. 99-100  0738-1360/00 $3.00 + .00
Printed in the U.S.A. All rights reserved.  Copyright © 2009 MRE Foundation, Inc.
Preface
A Nested Logit Model of Recreational
Fishing Demand in Alaska 
GEoRGE R. PARSonS
University of Delaware
JAMES L. AnDERSon
University of Rhode Island
The lead article in this issue of Marine Resource Economics by Richard Carson, Mi-
chael Hanemann, and Thomas Wegge is well known to many environmental and natural 
resource economists. It was originally presented at the Western Economic Association 
Meetings in Lake Tahoe, nevada in 1989 and is a shortened version of their 1987 report 
to the Alaska Department of Fish and Game. It was one of the first applications of the 
random utility maximization (RUM) model to recreation demand and one of the earli-
est examples of integrated bio-economic modeling used for actual policy purposes. The 
research was never published, but it was pivotal in launching the application of RUM 
models to recreation demand along with Bockstael, Hanemann, and Strand’s (1984) 
University of Maryland report to the US Environmental Protection Agency.  Given its 
importance, we thought it would be useful to make it more easily accessible and to rec-
ognize its contribution. When we invited the authors to submit the article, they expressed 
enthusiasm, so we proceeded.
  We decided to publish the article in its original form with the exception of minor edi-
torial changes. We asked Richard, Michael, and Thomas to write a foreword to the paper 
that would give readers historical context and include some discussion of the issues they 
confronted in designing and estimating the model. We also asked Kerry Smith and Ted 
McConnell to review the foreword, adding the perspective of two practitioners who were 
actively working on similar research at the time. Their comments have been incorporated 
in the foreword, and we thank them for their input. We also asked Richard to make the 
more comprehensive report, survey, and data available. These can now be found at http://
www.econ.ucsd.edu/~rcarson/ under “Alaska Fishing Data/Report.” The survey is includ-
ed in the report. 
  Random utility maximization models changed the landscape of nonmarket valuation 
in the 1980s for both revealed and stated preference analyses. The impact was striking on 
recreation demand modeling. The profession moved from largely single-site continuous 
demand models set in a seasonal timeframe and estimated using least squares, to ran-
dom utility models of many sites (hundreds or more) set in a single choice occasion and 
estimated using discrete choice econometrics. At the time, analysts were grappling with 
ways to account for substitute sites and conduct welfare analysis for quality changes at 
one or more sites in plausible and theoretically acceptable models. Inroads were made 
George R. Parsons is the program director and a professor of Marine Policy at the University of Delaware, Col-
lege of Marine and Earth Studies, 307 Robinson Hall, newark, DE 19716 (gparsons@cms.udel.edu). He is also 
an associate editor Marine Resource Economics. James L. Anderson is the chair and a professor in the Depart-
ment of Environmental & natural Resource Economics at the University of Rhode Island, 1 Greenhouse Rd., 
Kingston, RI  02881 (jla@uri.edu).  He is also the editor of Marine Resource Economics.Parsons and Anderson 100
with varying parameter models, systems of demand equations, pooled single-site models, 
and hedonic travel cost models. While each of these had some appeal and still see use in 
some modern applications, for all practical purposes when the RUM model was intro-
duced and successfully applied, it pushed most other modeling approaches aside. It told 
a realistic story, allowed for many sites, and easily accommodated welfare analysis for 
quality changes and site closures for a single or many sites. As the econometrics of dis-
crete choice analysis advanced in other areas of applied microeconomics and computing 
power grew (including software packages to accommodate estimation), the transition to 
RUM-based modeling was in full swing. 
  Carson, Hanemann, and Wegge’s research was an instrumental part of this transition, 
leading the way in many respects, and foreshadowing many of the problems that we still 
wrestle with today.  Although more than two decades have passed since the research was 
completed, it is still an enlightening read—not only for its historical importance to recre-
ation demand modeling but also for insights it may have for contemporary analysis.   
  
    
   

Preface: A Nested Logit Model of Recreational Fishing Demand in Alaska

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