Elsevier

Tourism Management

Volume 52, February 2016, Pages 173-186
Tourism Management

Determinants of demand for recreational walking trails in Ireland

https://doi.org/10.1016/j.tourman.2015.05.028Get rights and content

Highlights

  • Trail attributes can influence users' marginal willingness of to pay for/travel to trails.

  • Endowment and infrastructure attributes are primary factors affecting trail demand.

  • Flat or valley-endowed trails and infrastructure positively affect demand.

  • Robustness checks suggest farmer access agreements may also be important.

  • Mountain/hill, forest, coastal/lake trails, and income, negatively impact demand.

Abstract

The purpose of this work is to explore how recreational walkers' marginal willingness to pay for, or travel to, Irish trails is affected by trail attributes. We use principal components data reduction, z-score data normalization, and OLS regression analyses on two stated preference datasets covering 302 Irish and foreign visitors, and recreational experts, across 15 sites in Ireland between 2005 and 2010. Findings reveal that endowment and infrastructure attributes emerge as primary influences affecting demand. In particular, flat or valley-endowed trails, and infrastructure, including signage, amenities, and maps or leaflets positively affect demand. Additionally, robustness checks suggest farmer access agreements may also be important. On the other hand, we find that mountain or hill, forest, and coastal or lake trails, as well as income, seem to be negatively associated with demand. Finally, we are able to use these in-sample results to extrapolate demand for a set of new relatively unimproved trails in an out-of-sample demand prediction exercise.

Introduction

An important question within the tourism literature concerns the factors that are empirically associated with user's willingness to pay for (WTP) or to incur travel costs (TC) to visit recreational walking trails. The national Irish significance of these trails is highlighted by Fitzpatrick and Associates (2005) who estimated the total direct economic impact of trail usage in the Republic of Ireland (ROI) at €307 million. Having reliable predictors of aspects of tourists' demand can assist planners and investors to identify which trails to develop, and how to develop and manage them given limited available funds. Such contributions can better allow recreational walking sites to optimally contribute to tourism based job creation and economic growth, environmental sustainability, rural development, and income diversification (Ahtikoski et al., 2011, Arabatzis and Grigoroudis, 2010, Bennett et al., 2003, Bowker et al., 2007, Hovardas and Poirazidis, 2006, Schirpke et al., 2013, Siderelis and Moore, 1995, Tyrväinen et al., 2014, Udaya, 2003).

Policy makers concerned with trail development are becoming increasingly aware of the dangers of developing trails in multiple jurisdictions which involve social costs but do not enhance tourism revenues (Durbin and Ratrimoarisaona, 1996, Howley et al., 2012, Shonkwiler and Englin, 2005). They are opting instead for recreation enhancements that target high value trails that bring significant regional economic benefits, (Christie, Crabtree, & Slee, 2000). The result is that a few trails are chosen, but they are properly resourced primarily for their economic impact. Identifying such trails is not a trivial matter since a multitude of factors may affect the economic performance or desirability of any given trail. Further, imprecise estimates of regional economic effects associated with trail improvements may result unless a comprehensive set of factors are considered (Loomis, 1995). However, with a few exceptions (Arabatzis and Grigoroudis, 2010, Juutinen et al., 2011, Kelley and Evans, 2011, Neuvonen et al., 2010) most of the trail literature considers the effects of a particular dimension of trail attributes on the economic performance of a trail in isolation (Barry, van Rensburg, & Hynes, 2011). We are not aware of any work in the environmental economics literature that considers how several dimensions of attributes simultaneously affect the economic impact of a trail, and then uses this information to predict future trail choices/demands.

In the most general sense two complementary categories of analysis may be used to provide the aforementioned analysis, qualitative and quantitative methods. In terms of qualitative approaches a number of papers use focus groups, participatory approaches, participant observation, autoethnography and in-depth interviewing to examine trail experiences and factors that affect trail use (Henderson, 2006, Lowery and Morse, 2013, Mullins, 2014). Work described in McIntyre, Moore, and Yuan (2008) shows how qualitative “meaning – based” interpretive evaluations of trail experiences can be used to determine trail demand and usage decisions. Such an approach can be very useful for identification of high demand trails if they are clearly distinct in terms of attributes (as trails might be across countries). However, making more nuanced or intra-country demand predictions for trails which are distinguished by more subtle differences might be difficult. The complementary but alterative quantitative method of this work, and of the authors summarized in Section 2, is more relevant for the particular research questions of this paper; i.e. precise identification of the relative demands for the somewhat similar trails residing in our Irish sample.

Importantly, the approach used in this study does indeed consider a variety of trail attributes across multiple dimensions to identify prospective high value trails. This information about trail attributes' marginal demand associations can aid in the trail selection and investment process by providing overall demand, user welfare, and economic impact estimates in settings with the absence of survey data (Bowker et al., 2007, Lieber and Fesenmaier, 1985, Neuvonen et al., 2010, Schirpke et al., 2013). In terms of policy relevance, this work can serve to inform national policy makers about where and how best to invest limited rural development funds. This can aid in the development of recreational services and tourism so as to provide alternative sources of income in rural areas facing declines in primary production and agricultural land abandonment (Ahtikoski et al., 2011, Kelley et al., 2013, Neuvonen et al., 2010, Place, 1991, Schirpke et al., 2013, Slee et al., 1997, Tyrväinen et al., 2014, Vail and Hultkrantz, 2000).

More specifically, policy contributions relate to specific planning, park development, and trail design processes by identifying elements related to biotic, anthropogenic, and infrastructure features to include in newly created or upgraded trails (Arnberger et al., 2010, Bestard and Font, 2009, Graefe and Burns, 2013, Juutinen et al., 2011, Lieber and Fesenmaier, 1985, Lynn and Brown, 2003, Neuvonen et al., 2010, Reichhart and Arnberger, 2010, Schirpke et al., 2013). Additionally, results can contribute to cluster group targeting efforts, i.e. identifying the largest groups of users most likely to gain the most from particular trail investment or development activities (Kil et al., 2012, Sevenant and Antrop, 2010). Finally, results of quantitative analysis such as this can provide insights about how to manage social interactions and use conflicts (Arnberger et al., 2010, Reichhart and Arnberger, 2010), and can provide insights into alternative market mechanisms, such as the landscape and recreational values trading mechanism, which could better link up users and land owners so as to eliminate uncompensated externalities (Tyrväinen et al., 2014).

This study has two principle goals. First, what are the relative magnitudes of the associations among a diverse set of trail attributes across multiple dimensions, and users' demands in terms of WTP for, or incur TCs to, Irish trails? Second, what are the predicted WTPs and TCs for a new set of trails for which attribute indicators were recently collected, but for which we have no demand estimates; and can these predictions be used to target high value trails for development.

To address these research questions we perform analyses upon two independent survey datasets representing 15 separate trails and 302 respondents. For the first in-sample, previously published dataset (Buckley, van Rensburg, & Hynes, 2009), we relate multiple attributes' indicators to available stated and revealed measures of demand using our new analysis methodology. This allows us to identify significant influences and compare the relative magnitudes of the associations among attributes and two measures of demand (WTP and TC). We then extrapolate out-of-sample WTP and TC for a new set of surveyed trails by combining experts objectively rated attributes for these trails with the marginal demand parameters derived from the in-sample analysis. Finally, as suggested by Bowker et al. (2007), we provide suggestions about which of these new trails, conditional upon attributes, predicted demands, and improvement costs, would provide highest investment return.

The present study adds to the existing literature in several ways. First, in terms of the broad contributions to the academic literature, the study extends research on non-market valuation of land-based recreational and environmental amenities in other countries (de Aranzabal et al., 2009, Hanley and Ruffell, 1993, Arriaza et al., 2004, Kline and Wichelns, 1996, Pruckner, 1995, Scarpa et al., 2000b), and for Ireland in particular (Buckley et al., 2009b, Buckley et al., 2009a, Clinch and Murphy, 2001, Curtis, 2002, Davis and O'Neill, 1992, Fitzpatrick and Associates, 2005, Mill et al., 2007, Scarpa et al., 2000a). Second, it is one of the few applications to consider trail attributes across multiple dimensions and it builds on the work of (Arabatzis and Grigoroudis, 2010, Juutinen et al., 2011, Kelley and Evans, 2011, Neuvonen et al., 2010) by identifying key factors that are likely to influence the regional economic impact of a trail and ultimately to use this information to predict future trail choices.

Section 2 reviews the earlier literature, their contributions and limitations, and provides motivating precedents for the attributes and methods employed in the current work. Section 3 describes the data sources and variables. Section 4 summarizes the empirical methodology and robustness checks applied. Section 5 presents and discusses our key results, and Section 6 provides a concluding discussion highlighting key demand drivers, the consistency of our results with earlier literature, and discusses the policy implications of our findings. Acronyms in this study include: ordinary least squares (OLS), principal components analysis (PCA), principal component (PC), travel cost (TC), willingness to pay (WTP), and Republic of Ireland (ROI).

Section snippets

Earlier literature

A goal of this work is to investigate how recreational walkers' marginal willingness to pay for, or travel to, Irish trails is influenced by a broad set of trail attributes across multiple dimensions. The relevant literature is reviewed first in terms of the methodologies employed in each of the studies, and second, in terms of the different attribute dimensions which we classify according to five categories - Control, Endowment, Infrastructure, Access, and Social.

A number of earlier studies

Data and variables

The datasets for this study were assembled from a previously published stated preference survey of respondents in 2005–06, and from a new 2009–10 survey of recreational trail experts. The former in-sample dataset asked respondents at three walking sites to subjectively report their WTPs or TCs, at multiple recreational walking sites. Note that this survey questionnaire can be obtained from Buckley, van Rensburg, et al. (2009). The latter out-of-sample data summarizes experts' objective ratings

Empirical methodology

Our empirical method follows from Kelley, 1998, Jeserich et al., 2012, Laepple and Kelley, 2013, and van Rensburg et al. (2015), and allows us to mitigate a variety of statistical problems and identify the relative influences of individual trail attributes. Several robustness checks confirm that our approach provides meaningful results. The goal of the analysis is to determine how a comprehensive set of trail attribute indicators A are associated with two measures of demand for trails D(WTP,TC)

Results

Our in-sample results are based on an analysis of 21 control, infrastructure, endowment, and accessibility trail attributes subjectively reported by 290 survey respondents, and two measures of stated demand including WTP and TC. This data was collected in the ROI during 2005–06 across three trail sites. Out-of-sample demand extrapolation was then conducted for 12 additional trail sites for which we had objectively reported (by experts) trail attributes. The extrapolation can inform policy

Discussion

This paper investigates the influence of control, endowment, infrastructure, and access attributes on survey respondents' willingness to pay for, and travel to, recreational walking sites in the Republic of Ireland (ROI) during 2005–06. Based upon these results, we extrapolate what the WTP and TC would be for 12 new relatively unimproved walking sites surveyed in 2009–10. To date, little work has rigorously investigated the relative influences of a comprehensive set of site attributes and

Hugh Kelley joined Oxford Brookes University, Oxford, Department of Accounting, Finance, and Economics in 2015. He is a Professor of Economics. He obtained a B.Sc. (Economics) from the University of Wisconsin–Madison and a PhD (International Economics) from the University of California–Santa Cruz. Dr Kelley has previously Directedthe distance learning rural development BSc program (RUTAC) operated in collaboration among NUI Galway, NUI Maynooth, UCC, and UCD. He has also Directed the research

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    Hugh Kelley joined Oxford Brookes University, Oxford, Department of Accounting, Finance, and Economics in 2015. He is a Professor of Economics. He obtained a B.Sc. (Economics) from the University of Wisconsin–Madison and a PhD (International Economics) from the University of California–Santa Cruz. Dr Kelley has previously Directedthe distance learning rural development BSc program (RUTAC) operated in collaboration among NUI Galway, NUI Maynooth, UCC, and UCD. He has also Directed the research center ICERTS (Irish Center for Rural Transformation and Sustainability). Prior to coming to Oxford Brookes, Dr. Kelley was a tenured Associate Professor at NUI, Galway from 2006 to 2015 and at at Copenhagen University in FOI's Division of International Economics from 2006 to 2008; and an Assistant Professor at Indiana University-Bloomington Department of Economics, from 2000 to 2005. He also completed a NIMH Post Doctoral program in Mathematical Modeling of Cognition at Indiana University's Department of Psychology. Dr Kelley conducts collaborative research with researchers at a number of research institutes and Universities around the world and has held positions as Adjunct Professor at Indiana University's Department of Psychology, as a Senior Research Associate at the Workshop Political Theory and Policy Analysis at Indiana University, as a Research Fellow at the Max Planck Institute in Bonn, Germany, and as Visiting Faculty at Brandeis University's Department of Economics. He has also presented his work widely at conferences and seminar series in the United States, Canada, Germany, Netherlands, Greece, Ireland, Denmark, and Japan. He has published papers in Ecological Economics, Economic Inquiry, Journal of Economics and Management Strategy, Land Use Policy, Journal of Agricultural Economics, Journal of Mathematical Psychology, and Journal of Environmental Management, and co-authored chapters in a number of books. Dr Kelley has also pursued an active funded research agenda. In addition to numerous travel and small University grants he has contributed to three large research grants from the U.S. National Science Foundation, an FP7 Framework grant from the European Commission, and an Irish Department of Agriculture and Food Research Stimulus grant altogether helping garner research funds worth over 4 million euro since 2000.

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