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Cultural heritage and the attractiveness of cities: evidence from recreation trips

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Abstract

Many cities are trying to attract tourists by investing in urban amenities. Cultural heritage is an important example, and substantial investments are needed to keep ancient inner cities and characteristic monumental buildings in good shape. The costs of these policies are usually clear, and the benefits are often much more difficult to assess. This paper attempts to fill part of this gap by studying the destination choices of urban recreation trips that have urban recreation as the main travel motive. We estimate a discrete choice model for destination choice that takes into account the potential importance of unobserved characteristics. The model allows us to compute the marginal willingness-to-travel for destinations offering more cultural heritage, which we measure as the area of the inner city that has a protected status because of the cultural heritage that is present there.

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Notes

  1. Day trips are defined as a trip for recreation, for which the consumer leaves the residence at least 2 h, without an overnight stay involved (Statistics Netherlands 2013a).

  2. Noonan and Krupka (2010) provide an interesting analysis on which objects are more likely to be designated with some sort of cultural heritage status.

  3. The data are collected by NBTC (Dutch Bureau of Tourism and Conferences) and NIPO (Dutch Institute for Public Opinion—part of the TNS group).

  4. We assume that the distance travelled for trips that start and end in the same municipality equals 5 km. Our assumption rests on the average distance between the mid-points of a municipality and its borders.

  5. This corresponds mostly to the dominant aspect of the region, such as beaches, hills, and/or lakes.

  6. If desired, it can be further translated into a monetary measure, using information about travel speed and the value of travel time, or transport costs.

  7. The argument is similar as in the mixed logit model. Indeed, substitution of (3) in (2) and of the result in (1) leads to an equation that is analogous to the error components formulation of the mixed logit model. For a discussion of mixed logit, see Train (2009), and for further discussion of specification (3) of the coefficients β, see Bayer et al. (2004) and Van Duijn and Rouwendal (2013).

  8. Note that the average impact of unobserved heterogeneity will be included in the constant term that we include in (6).

  9. The net effect of the heritage variable cityscape on utility remains positive for 93 % of the municipalities under study.

  10. That is, we have treated them as separate tourist regions, as was explained in Sect. 2.

  11. We start from the first line of (5), and slightly abusing the notation, we use j = CH, CHCAT for the characteristics cultural heritage and cultural heritage times the number of catering facilities.

  12. The statistically insignificant coefficients of the first step of the nested logit model (in Table 3) have been set to 0. As a result, the marginal willingness-to-pay for recreationists who do not have children in the household aged 12–17 or are of age 51–99 is that of the size of the average recreationist, 1.303 km.

  13. The average area of protected cityscape for municipalities that have such an area is 1.09 km2.

  14. Our current data unfortunately do not allow for the investigation of this issue.

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Acknowledgments

We would like to thank Henri de Groot and Mark van Duijn for their insightful comments. We thank RCE, ABF Research, and Statistics Netherlands for providing us with excellent data. We would also like to thank the members of the Department of Spatial Economics for helpful comments during discussions. Ruben van Loon (2013) gratefully acknowledges CLUE, NICIS, and VU University for their financial support.

Author information

Authors and Affiliations

  1. Department of Spatial Economics, VU University Amsterdam, Room 4A-11, De Boelelaan 1105, 108 HV, Amsterdam, The Netherlands

    Ruben van Loon, Tom Gosens & Jan Rouwendal

  2. Tinbergen Institute, Gustav Mahlerplein 117, 1082 MS, Amsterdam, The Netherlands

    Jan Rouwendal

Authors
  1. Ruben van Loon
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  2. Tom Gosens
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  3. Jan Rouwendal
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Corresponding author

Correspondence to Jan Rouwendal.

Appendices

Appendix 1: Tourist region classification for the Netherlands

See Table 6.

Table 6 Descriptive statistics of the tourist regions as classified by Statistics Netherlands
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Appendix 2: Graphical presentation of the alternative-specific constants

This appendix contains graphs that show the value of the alternative-specific (municipality-specific) constants across the Netherlands, which have been estimated in the nested logit models with three different heritage indicators. Those municipalities that receive a darker tone are often larger than those with a lighter tone (Figs. 2, 3, 4).

Fig. 2
figure 2

Value of the alternative-specific constants with “protected cityscape area size” as heritage indicator

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Fig. 3
figure 3

Value of the alternative-specific constants with “national monuments” as heritage indicator

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Fig. 4
figure 4

Value of the alternative-specific constants with “museums” as heritage indicator

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Appendix 3: Robustness analysis estimation results

See Tables 7, 8, 9, and 10.

Table 7 Estimation results of the nested logit destination choice model, with “Number of national monuments” as heritage indicator
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Table 8 Estimation results of the linear regression models, with “Number of national monuments” as heritage indicator
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Table 9 Estimation results of the nested logit destination choice model, with “Number of museums” as heritage indicator
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Table 10 Estimation results of the linear regression models, with “Number of museums” as heritage indicator
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Appendix 4: Scaling parameters for the tourist region nests

Table 11 shows the scaling parameters λ k for each tourist region. The scaling parameter is set to a value of 1 for tourist regions 1, 2, and 6. The low number of visits to these tourist regions is perhaps responsible for the impossibility to estimate a parameter in the utility-consistent range of 0 and 1. As the tourist regions of the 4 largest agglomerations consist of only one municipality each, the scaling parameter is also set to the value of 1 for these tourist regions. Interpreting the estimated models in terms of utility maximization would otherwise be problematic.

Table 11 Scaling parameters for the tourist region nests, with different heritage indicators
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Appendix 5: First-stage instrumental variables regression estimation results

This appendix presents the first-stage instrumental variables regression estimation results for each of the specifications. For the sake of brevity, we do not report the coefficients for the tourist region dummies, which are available at request with the authors (Tables 12, 13, 14).

Table 12 First-stage instrumental variables regression estimation results with “Protected cityscape area size” as heritage indicator
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Table 13 First-stage instrumental variables regression estimation results with “Number of national monuments” as heritage indicator
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Table 14 First-stage instrumental variables regression estimation results with “Number of museums” as heritage indicator
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van Loon, R., Gosens, T. & Rouwendal, J. Cultural heritage and the attractiveness of cities: evidence from recreation trips. J Cult Econ 38, 253–285 (2014). https://doi.org/10.1007/s10824-014-9222-5

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