Abstract
One of the key strengths of SMILE is its facility to combine spatial data from many diverse sources in order to examine economic, social and environmental issues of importance to the rural economy in Ireland. Indeed, spatial context lies at the heart of many aspects of the rural economy and SMILE, as a modelling and data infrastructure, can be usefully applied in conjunction with techniques such as geographic information systems (GIS) analysis and microeconometrics to examine such issues. One such area of interest is rural tourism. Rural tourism is now an important contributor to rural development in Ireland given the long term decline of agriculture, particularly in its potential for stimulating employment and providing a viable option for off-farm diversification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In the recreation demand modelling literature, such models are routinely referred to as random utility (maximisation) models (RUMs).
- 2.
According to Hanley et al. (2003: 12), “whether to use objective or subjective measures of these characteristics has proved another tricky issue”.
- 3.
In a recreation demand modelling context, this estimate could then be combined with an estimate of average consumer surplus per visit to site j in order to derive the total amenity value of the site. The focus here, however, is on estimating the demand across sites in order to consider the spatial pattern of rural tourism and recreation activities.
- 4.
See Hynes et al. (2007) for a full discussion of the data gathering exercise as well as a discussion of the descriptive statistics for the sample.
- 5.
In the first instance the proportion of kayakers in each county was estimated using survey data from the ICU and then multiplied by an estimate of the total number of kayakers in Ireland to develop estimates of the total number of kayakers by county. The next step involved allocating the kayakers in each county by ED within that county based on the spatial distribution of the total (working-age) population in Ireland in 2002 by ED using small area population statistics data from the CSO. Specifically, the proportion of a county’s (working-age) population in each ED was used to allocate kayakers, hence effectively matching the proportion of a county’s kayakers in each ED to the proportion of a county’s (working-age) population in each ED. The next step involves assigning personal, socio-economic and whitewater kayaking-specific characteristics to each individual kayaker in the synthetic population and this was done by replicating profiles from the Hynes et al. (2007) survey and randomly allocating them to individuals in the synthetic population.
- 6.
Kayakers are concentrated, as expected, in the principal urban areas, where populations are greatest and university and non-university kayaking clubs tend to be located. The correlation between the estimated number of kayakers per county and the actual number of kayaking clubs per county is 0.82.
- 7.
A centroid of an ED is defined as its geometric centre. It can be thought of as the point within an ED on which it would balance when placed on a needle, assuming that the ED was a smooth flat surface.
- 8.
Overdispersion in the data can easily be tested for in the estimation process.
- 9.
LR test of α = 0: chibar2(1) = 5014.83; Prob > = chibar2 = 0.000.
- 10.
A test for IIA is presented subsequently.
- 11.
All models were estimated using NLOGIT, which can take account of outcomes data when in the form of frequency counts. The frequencies must be non-negative integers for all choice outcomes and may be equal to zero.
- 12.
These four rivers were: The Liffey, Clifden Play Hole, The Boyne, and The Boluisce. The χ2 statistics from the tests on the other seven rivers ranged from 1.46 to 25.53.
- 13.
It should be stressed that the nests in Fig. 13.2 are somewhat arbitrary and numerous other combinations or groupings or rivers are also possible. For example, The Curragower and Clare Glens could be considered as part of the ‘West’ group or a two-nest approach focussed on an East–west division could also be examined. Overall it is obvious however that these nests are likely to differ across individuals depending on geographical location of individuals and this reduces the validity of the results of the nested logit model in Table 13.4. However, it is also worth noting that they do not differ greatly from the RPL estimates.
References
Cameron AC, Trivedi PK (1998) Regression analysis of count data, vol 30, Econometric society monographs. Cambridge University Press, Cambridge, UK/New York
Carson RT, Hanemann WM, Wegge T (1989) A nested logit model of recreational fishing demand in Alaska. Paper presented at the annual western economic association conference, Lake Tahoe
Creel MD, Loomis JB (1990) Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California. Am J Agric Econ 72:434–441
Cullinan J, Hynes S, O’Donoghue C (2008) Estimating catchment area population indicators using network analysis. Ir Geogr 41:279–294
Feather P, Hellerstein D, Tomasi T (1995) A discrete-count model of recreation demand. J Environ Econ Manag 29:214–227
Gomez IA, Ozuna T (1993) Testing for overdispersion in truncated count data recreation demand functions. J Environ Manage 37:117–125
Greene WH (1993) Econometric analysis, 2nd edn. MacMillan, New York
Grogger JT, Carson RT (1991) Models for truncated counts. J Appl Econom 6:225–238
Hanley N, Shaw WD, Wright RE (2003) Introduction. In: Hanley N, Shaw WD, Wright RE (eds) The new economics of outdoor recreation. Edward Elgar, Northampton/Cheltenham
Hanley N, Shogren JF, White B (2007) Environmental economics: in theory and practice, 2nd edn. McMillan, Basingstoke/New York
Hausman J, Leonard G, McFadden D (1995) A utility-consistent, combined discrete choice and count data model: assessing recreational use losses due to natural resource damage. J Public Econ 56:1–30
Hausman J, McFadden D (1984) Specification tests for the multinomial logit model. Econometrica 52:1219–1240
Herriges JA, Kling CL, Phaneuf D, Herriges JA, Kling CL, Phaneuf D (1999) Valuing recreation and the environment. In: Herriges JA, Kling C (eds) Corner solution models of recreation demand: a comparison of competing frameworks. Edward Elgar, Northampton
Hynes S, Hanley N (2006) Preservation versus development on Irish rivers: whitewater kayaking and hydro power in Ireland. Land Use Policy 23:170–180
Hynes S, Hanley N, Garvey E (2007) Up the proverbial creek without a paddle: accounting for variable participant skill levels in recreational demand modeling. Environ Resour Econ 36:413–426
Johnstone C, Markandya A (2006) Valuing river characteristics using combined site choice and participation travel cost models. J Environ Manage 80:237–247
McFadden D, Train K (2000) Mixed MNL models for discrete response. J Appl Econom 15:447–470
Morey E, Rowe R, Watson M (1993) A repeated nested-logit model of Atlantic salmon fishing. Am J Agric Econ 75:578–592
Parsons GR, Jakus PM, Tomais T (1999) A comparison of welfare estimates from four models for linking seasonal recreational trips to multinomial logit models of site choice. J Environ Econ Manage 38:143–157
Parsons GR, Kealy MJ (1995) A demand theory for number of trips in a random utility model of recreation. J Environ Econ Manage 29:357–367
Parsons GR, Massey D (2003) A RUM model of beach recreation. In: Hanley N, Shaw WD, Wright R (eds) The new economics of outdoor recreation. Edward Elgar, Cheltenham/Northhampton
Shonkwiler JS, Shaw WD (1997) The aggregation of conditional demand systems. Paper presented at the annual W133 regional project annual meetings, Monterey
Zandersen M, Termansen M, Jensen FS (2007) Evaluating approaches to predict recreation values of new forest sites. J Forest Econ 13:103–128
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cullinan, J., Hynes, S., O’Donoghue, C. (2013). Modelling the Spatial Pattern of Rural Tourism and Recreation. In: O'Donoghue, C., Ballas, D., Clarke, G., Hynes, S., Morrissey, K. (eds) Spatial Microsimulation for Rural Policy Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30026-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-30026-4_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30025-7
Online ISBN: 978-3-642-30026-4
eBook Packages: Business and EconomicsEconomics and Finance (R0)