Economic impacts of hunting activities reveals opportunities for landowners to capitalize on apparent market demand for fee-access hunting. This paper discusses the marginal values of hunting package attributes. The results will provide landowners the information needed to make optimal management decision.Resource /Energy Economics and Policy,

Buller, Virginia

Hudson, Darren

Parkhurst, Gregory M.

Whittington, Andrew

English

Research Papers in Economics

  1
The Value of Hunting Package Attributes  
Virginia Buller 
1, Darren Hudson, Greg Parkhurst, Andrew Whittington 
 
 
 
Selected Paper prepared for presentation at the 
Southern Agricultural Economics Association Annual Meetings 
Little Rock, Arkansas, February 5-9, 2005 
 
 
Abstract 
 
Economic impacts of hunting activities reveals opportunities for landowners to capitalize 
on apparent market demand for fee-access hunting.  This paper discusses the marginal 
values of hunting package attributes.  The results will provide landowners the 
information needed to make optimal management decision. 
 
Introduction 
A survey by the United States Wildlife and Fisheries Department in 2001 
estimated that 82 million residents fished, hunted, and watched wildlife. In pursuing these 
recreational activities, the residents spent over $108 billion dollars.  These expenditures 
contributed to millions of jobs in related industries and businesses as well as supporting 
wildlife-related recreation.  The money spent on licenses and taxes on hunting and fishing 
contribute to many conservation efforts across the United States as well as economic 
development in rural areas. 
Per State hunting expenditures were also available in the 2001 survey.  Total 
expenditures for hunters ages 16 and over were separated within each state.  In 
                                                 
1 V. Buller, Graduate Student, D. Hudson, Associate Professor, G. Parkhurst, Assistant Professor and 
A.Whittington, Graduate Student, Department of Agricultural Economics, Mississippi State University.  
 
Copyright © 2005 by Virginia Buller, Darren Hudson, Greg Parkhurst and Andrew Whittington.  All rights 
reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, 
provided that this copyright notice appears on all such copies. 
 
This research was funded by the Wildlife Economic Enterprises grant program through the United States 
Department of Wildlife and Fisheries.     2
Mississippi residents spent $360 million on hunting expenditures. Trip related expenses, 
which included food, lodging, transportation, and other were approximately $132 million 
or 37% of the total hunting expenditures.  In that $132 million, food and lodging 
expenses totaled $73 million.  On average, trip related expenditure per hunter was $370.  
These hunting expenditures provide economic development in small communities 
across the nation.  Local businesses, motels, small cafes, gas stations and grocery stores 
benefit from this source of revenue.  Guide services, outfitters and bed and breakfast 
operations are also benefiting the local economy (Hondur et al).  
The large economic impact of hunting activities reveals opportunities for 
landowners to capitalize on apparent market demand for fee-access hunting. Messionier 
and Luzar stated wildlife is considered to be owned by the state and therefore cannot be 
bought and sold.  Access to wildlife, on the other hand, can be bought and sold.  This 
access is controlled by private landowners who may gain additional revenue by charging 
a fee for access to the land.  Public land is another way to gain access to wildlife but a fee 
cannot be charged to gain access to these lands.  
Markets for access to private lands have increased over the years primarily 
because of overcrowding in the public land areas.  As a result many hunters are willing to 
pay a fee to gain access to private lands, the amount of which depends on the amenities 
provided.  Landowners currently not involved in fee access enterprises are limited by the 
amount of quality information on which to base decisions concerning the provision of 
amenities.  Economic analysis can be useful in providing landowners the information 
they need to make an informed decision on whether or not to provide recreational access 
to their lands.  Some results from this study may indicate what amenities hunters may 
                                                                                                                                                 
   3
find important, which would be very useful in management decisions.  A number of 
studies have been conducted to elicit these values.  
In a study done by Gan and Luzar, a conjoint analysis was used to analyze 
waterfowl hunting in Louisiana. Data was collected from a survey of waterfowl hunters 
and ordered logit was used to estimate willingness-to-pay for recreation experience 
attributes.  This study found that waterfowl hunters evaluate each available hunting 
alternative in terms of its attributes.  Conjoint analysis was used to model consumer 
preferences for multiattribute choices, but this approach is sensitive to design, 
implementation and interpretation.  In another study done by Green, Grijalva and Kroll, 
the willingness-to-pay for hunting club memberships was estimated using the contingent 
valuation method (CVM).   
Most studies have used hypothetical methods to estimate values of these 
amenities.  According to Lusk, research shows that subjects overstate the amount they are 
willing to pay for a good when hypothetical approaches are used.  This study will add to 
the existing literature by taking a number of fee access hunting providers and examining 
actual market prices for hunting packages to estimate the marginal value of fee-access 
hunting amenities.  With these market values, the land owner can then make an informed 
decision on how to manage their fee-access hunting enterprise.  
Conceptual Framework 
Fee access hunting, or hunting packages, is an important market with significant 
economic impacts. Consumers or hunters in this case, derive utility from the good’s 
characteristics rather than the good itself (Lancaster).  The price of these packages is a 
composite of all the attributes included in the package.  The marginal implicit price can   4
be determined by taking the partial derivative of price with respect to each individual 
attribute. The expected service a commodity offers provides the characteristics associated 
with that commodity. The level of service provided is a measure of the good’s utility 
(Louviere et al). 
By examining market information on a given commodity, a price function may be 
estimated that provides critical information about the marginal value that consumer’s 
place on the attributes of a good. Because provision of attributes like food, lodging, 
guides, etc., are expensive in fee access hunting, it is useful to understand the value that 
consumers place on these attributes so that lease providers can make an informed 
decision about whether to offer those attributes to consumers.  
Methods 
The data being used in this study was taken from packages offered by fee access 
hunting providers around Mississippi (see Appendix for listing of source finding). The 
data was retrieved from the Internet and advertisements given for fee based hunting. The 
data set consists of thirteen sample firms, which offer seventy-eight different hunting 
packages. The attributes contained within these packages include the number of days 
being hunted, bag limits, lodging, food, guide service, trophy fees, fishing, photography 
and species being hunted.  Packages could include one species in a given package or 
more than one.  
  A hedonic model of hunting package prices was estimated using ordinary least 
squares.  The marginal values of these attributes are estimated from a regression analysis 
where price is a function of these attributes.  The marginal implicit prices of each   5
attribute can be found by taking the partial derivative of price with respect to each 
individual attribute.  Price is a function of these attributes: 
) , , , , , , , , ( α L TF TR F Food L G S D f P =  
Where: D  equals the number of days being hunted.  S is the species being hunted (there 
may be multiple species hunts).  G  = 1 if whether guide services are provided; 0 
otherwise.  L = 1 if lodging is provided; 0 otherwise.  F  = 1 if food is provided; 0 
otherwise.  F = 1 if fishing is provided; 0 otherwise.  TR = 1 if transportation is provided; 
0 otherwise.  TF  = 1 if a trophy fee is present; 0 otherwise and  α L  is bag limit.  
We expect that number of days in the hunt, guide service, food, lodging, fishing 
and transportation will have a positive impact on the price of the package.  Bag limits and 
trophy fees should have a negative impact on price.  Also, there are likely species-
specific effects on the package price as well.  
  Species were separated into four groups, one for each of the following sets of 
species: deer (deer), duck/geese (DU), quail/pheasant, boar, turkey (QP), and dove (DV).  
Due to a high pair wise correlation between dove
2 and bag limit, the control for bag limit 
is dropped from the econometric model.  To discern the impacts of the remaining 
attributes on hunting lease prices, the following equation is estimated using least squares 
regression: 
() G Food L TF TR F QP DU Deer D P , , , , , , , , , =  
                                                 
2   Limit and doves had a correlation value of .75213, which may be related to the small number of dove 
hunts within the packages.  To correct for these relationships limits were dropped and doves were 
combined with quail and pheasant as the base species category.  Boar was also included in the base 
category along due to the small number of boar hunts in the data.     6
 
  The variables used in the regression analysis are listed with their descriptive 
statistics in Table 1.  A log-linear functional form was used to estimate the model.  In a 
log-linear model, the slope coefficient of the regressors gives the semi-elasticity, which is 
the percentage change in the regressand for a one-unit change in the regressor.  But this 
only occurs if the regressor is quantitative. In our model, all of our regressors were 
dummy variables.  Dove (DV) was used as the base species category so that all other 
species marginal values were relative to the base category.  
Results and Discussion 
The regression results can be found in Table 2.  Days, fishing and lodging were 
all positive and significantly different than zero.  The coefficient on days (D) has a value 
of .3087. This suggests that an additional day of hunting will increase the package price 
by 30% or $25.11 on average.  Adding fishing to the package would increase the package 
price by 25% or $20.93 on average.  Lodging would also increase the package price by 
84%.   
The species that were used in the model all had positive coefficients suggesting 
that all species increased package prices relative to the base category.  Quail and pheasant; 
the base category, impacted price the most with a coefficient value of 56%.  The quail 
and pheasant amenity will increase package price by $46.89 on average.  Deer hunting 
increased the package price by 53%, while ducks and geese increased the package price 
by 37%.  Deer hunting on average will increase package price by $44.37.  Deer included 
three amenities; bow, rifle and muzzleloader.  The coefficients for food contained 
negative values, it was not statistically significant.  Transportation, trophy fee, and guide   7
service were also not statically significant at the 5% and 1% levels.  All of the other 
coefficients were significant at the 5% and 1% levels.  The R-Squared Value is .8414, 
which suggests that 84 % of the variation in price is explained in the model.  
Conclusion 
Given these results a policy maker or landowner can better understand the 
management goals that need to be reached.  With these specific attribute values one can 
examine the effects of their hunting packages and reevaluate the needs concerned to their 
business.  One consideration for the landowner would be to include fishing when it is 
available.  It increases the package prices on average by $20.93.  Fishing is a low cost 
amenity for the landowner.  The only thing that the landowner may need to provide is a 
guide. After analyzing the results further study must be done to look at the added value 
that secondary hunts give to a primary hunting package. Other studies could be done to 
look at the income effect of each state on these hunting package prices.  The model that 
was derived served its purpose in telling us how these attributes affect the price of 
hunting packages.   
 
 
 
 
 
 
 
   8
 
 
 
References 
 
Green,C.B., T. Grijalva, and S. Kroll. Social Capital and the Value of Hunting Club  
  Memberships.  California State University. 
 
Hondur, N.M., Leistritz, F.L., and Bangsund, D.A. “Local Socioeconomic Impacts of the  
  Conservation Reserve Program.”  Agribusiness and Applied Economics Report      
 Number  476. April 2002. 
 
Lancaster, K.  “A New Approach to Consumer Theory.” Journal of Political Economy.   
  74(April 1966): 132-157 
 
Louviere, J., D. Hensher, and J. Swait.  Stated Choice Methods: Analysis and   
  Applications.  University Press. Cambridge, UK. 2000. 
 
Lusk, J.  “Using Experimental Auctions for Marketing Applications: A Discussion.”  
  Journal of Agricultural and Applied Economics.  35(August 2003): 349-360 
 
Messonier, L.M., and J.Luzar.  “A Hedonic Analysis of Private Hunting Land Attributes  
  Using An Alternative Functional Form.”  Southern Journal of Agricultural  
 Economics.  22(1990): 129-135 
 
United States Wildlife and Fisheries Department.  http://www.fws.gov.  March 02, 2004. 
 
 
 
 
 
 
   9
 
 
 
 Table 1.  Descriptive Statistics. 
Variable Mean  (Standard  Deviation) 
P (Price)  984.2307  (794.5496) 
D (Number of Days being hunted)  2.6865  (1.6759) 
Deer  0.5000 0.5031 
DU (Ducks or geese)  0.1666  0.3751 
DV (Dove)  0.0769  (0.2681) 
QP (Quail or Pheasant)  0.0512  (0.222) 
Food (Food is available)  0.7564  (0.4320) 
L (Lodging available)  0.8589  (0.3503) 
F (Fishing)  0.5384  (0.501745) 
TR (Transportation)  0.8717  (0.3364) 
G (Guide) .4358  (.4991) 
TF (Trophy Fee)  0.0641  (0.2465) 
Lα (Limit)  4.6794  (5.9182) 
 
 
 
 
   10
Table 2.  Regression Analysis Results. 
Variable Coefficient 
(Standard Error) 
t-statistic 
Constant  4.4276* 
(0.1878) 
23.575 
D  .3087* 
(0.0416) 
7.420 
Deer  0.5399* 
(0.1339) 
4.030 
DU  0.3766** 
(0.1572) 
2.395 
QP  0.5622*** 
(0.2924) 
1.923 
F  0.2555*** 
(0.1373) 
            1.860 
TR  0.0508 
(0.1519) 
.335 
TF  0.1895 
(0.2186) 
0.867 
L  0.8463* 
(.2085) 
4.058 
G  .01652 
(.1184) 
0.140 
Food  -0.0338 
(0.1854) 
-0.183 
 
* Statistically significant at the 1% level.  
** Statistically significant at the 5% level. 
*** Statistically significant at the 10% level.  
 
 
 
 
 
   11
Appendix 
 
Bayou River Outfitters. http://www.bayouriveroutfitters.com.  January 13, 2004. 
Chilli Creek.  http://www.chillicreek.com. January 13, 2004. 
Circle Bar Ranch.  http://www.circlebar.com.  February 05, 2004. 
Cypress Lodge.  http://www.cypresslodge.com.  February 05, 2004. 
Giles Island.  http://www.gilesisland.com.  February 05, 2004. 
Hunting Top10.  http://www.huntingtop10.com.  January 13, 2004. 
Mallard Manor.  http://www.mallordmanor.com.  February 05, 2004. 
McKenna’s.  http://www.mckennas.com.  February 05, 2004. 
The Panther Tract.  http://www.panthertract.com.  January 13, 2004. 
Tara Wildlife.  http://www.tarawildlife.com.  January 13, 2004. 
 

The Value of Hunting Package Attributes

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics