The Travel Cost Method – Some Pitfalls

The travel cost method is an essential tool in estimating the economic value of non-market recreational sites such as parks,  but its application is rarely straightforward.  Here are some of its pitfalls.

Originally posted 11/8/2012.  Re-posted following site reorganisation 21/6/2016.

There are three stages in a travel cost site valuation (1):

  1. Obtain data on visit rates, visitors’ travel costs, and perhaps other variables.  The data may be either at the level of individuals, or aggregated by zones of origin.
  2. Use regression analysis to estimate how the visit rate depends on travel cost and any other variables.
  3. Derive a demand curve for visits to the site, and find the associated consumer surplus which is a measure of the site’s use value.

Pitfall 1 – Data Collection via an Off-site Survey     Contacting every nth person on the electoral list by mailshot or telephone might seem a simple means of ensuring an unbiased dataset.  However, response rates could be low, and a high proportion of people might not have visited the site at all.  Hence a very large sample, with high data collection costs, might be needed to obtain a useful dataset.  Bias might also be introduced by a higher response rate from those who have visited the site.  Probably for these reasons, most travel cost valuations are based on on-site surveys  (2).

Pitfall 2 – Simplistic Analysis of Individual On-site Survey Data     Any on-site survey, whether via short interviews, or handing-out of questionnaires to be completed and returned, over-represents people who visit the site relatively often and excludes those who do not visit it at all.  Hence direct application to individual on-site data of standard regression techniques such as ordinary or weighted least squares will lead to inflated estimates of visit rates, and therefore of the site value.  The on-site individual approach, although the most common in recent literature, requires specialised techniques to correct for these sample characteristics (3).

Pitfall 3 – Zonal Bias in On-site Data     The older but still widely used zonal approach also begins with a survey of individuals, to provide raw data for aggregation at zonal level.  To minimise data collection costs, it might seem sensible to choose a sunny weekend when many visitors are on site.  However, this could result in disproportionate selection of visitors from more distant zones, leading to an inflated estimate of site value.  To avoid this type of bias, the sample may need to be stratified with respect to variables such as day of week, weather conditions, and location within a large, multi-entrance site (4).

Pitfall 4 – Inappropriate Treatment of Zero-Visit Zones     When using the zonal approach, it may be found that the on-site sample of visitors contains no visitors from certain zones. Such zero-visit zones can be awkward to handle in the regression analysis, and it can be tempting to drop them from the dataset, but this can lead to a biased valuation (see diagram below).  The basic principle here must be that any observed visit rate, positive or zero, is part of the data to be analysed (5).  It can however be appropriate sometimes to drop some zero-visit zones as a step in the data analysis, prior to the regression.  A dataplot may suggest an approximate choke price (travel cost above which the visit rate is zero).  It could then be appropriate to drop those zero-visit zones with travel costs above that price (see diagram).

Pitfall 5 – Underestimating the Frequency and Importance of Multi-Purpose Trips     Multi-purpose and multi-destination trips may take a variety of forms.  At one extreme is the person on a foreign holiday visiting a national park and other tourist sites.  However, visitors to urban parks are often engaged in short multi-purpose trips, perhaps involving meeting friends, shopping or going to a restaurant.  Multi-purpose and multi-destination trips complicate the determination of travel costs and, for the zonal approach, the assignment of individuals to zones of origin.  Every travel cost study needs a strategy for handling them at both the data collection and the analysis stages. There is a large literature on this problem (6), with no one-size-fits-all solution.

Notes and references

1.  A useful and accessible explanation of the travel cost method is Karasin L The Travel Cost Method: Background, Summary, Explanation and Discussion  http://www.ulb.ac.be/ceese/PAPERS/TCM/TCM.html

2.  A rare example of a travel cost valuation based on an off-site survey is described in Gum R L & Martin W E (1975) Problems and Solutions in Estimating the Demand for and Value of Rural Outdoor Recreation  American Journal of Agricultural Economics 57 pp 558-566.

3.  Englin E & Shonkwiler J S (1995)  Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand Under Conditions of Endogenous Stratification and Truncation  The Review of Economics and Statistics  77(1) pp 104-112.

4.  An on-site survey with stratified sampling is described in Rolfe J & Prayaga P (2007) Estimating Values for Recreational Fishing at Freshwater Dams in Queensland  The Australian Journal of Agricultural and Resource Economics 51 p 160.

5.  The principle is asserted in Christensen J B & Price C (1982) A Note on the Use of Travel Cost Models with Unequal Zonal Populations: Comment   Land Economics  Vol 58(3) p 399.  An example of a study explicitly stating that zero-visit zones are included in the analysis is Mendelson R, Hof J, Peterson G & Johnson R (1992)  Measuring Recreation Values with Multiple Destination Trips  American Journal of Agricultural Economics 74 p 931.

6.  See for example Loomis J (2006) A Comparison of the Effect of Multiple Destination Trips on Recreation Benefits as Estimated by Travel Cost and Contingent Valuation Methods  Journal of Leisure Research 38(1) pp 46-60.

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