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Valuing Local Environmental Amenity with Discrete Choice Experiments: Spatial Scope Sensitivity and Heterogeneous Marginal Utility of Income

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Abstract

Using discrete choice experiments we examine preferences for the spatial provision of local environmental improvements in the context of regeneration policies. Amenities we consider are: improvements to areas of open space, recreation facilities and other public spaces; street cleanliness; restoration of derelict properties; and the provision of paths dedicated to cycling and walking. We include the spatial scope of the policy as an attribute, making the trade-off between environmental amenity and its spatial provision explicit. We employ a novel estimator for average willingness to pay (WTP) for mixed logit models with a random cost coefficient, which is robust to the presence of price insensitive respondents and performs well relative to mixed logit estimation in WTP space. We find that the spatial scope of the policy affects both the intensity and heterogeneity of preferences, and that these effects are of statistical and economic significance.

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Notes

  1. While this study is the first to study marginal values for a broad range of local environmental amenities in the context of local regeneration schemes, the DCE approach has previously been used to evaluate preferences for amenities such as urban green spaces (Bullock 2006), improvements to city centres (Alberini et al. 2003) and urban river quality (Hanley et al. 2007).

  2. A more detailed description of the attributes presented to survey respondents can be found in the online appendix for this paper.

  3. This definition is consistent with with UK’s National Indicator (NI 195), which is measured at the Local Government level in England as a part of the regulatory framework for assessing the performance of Local Authorities. Note that the status quo (Grade C) is not the worst possible classification, but we do not consider a degradation in street cleanliness.

  4. While regeneration initiatives are typically funded from a variety of sources and initial capital outlays could be the responsibility of national or European initiatives, maintenance of improvements will most likely be the responsibility of Local Government. Note also that at the time of the survey, a 50 increase in council tax was approximately a 4–5% increase in the average household council tax bill.

  5. The assignment of attributes to blocks mitigates the potential complementarities between attributes within each block based on focus groups’ qualitative results.

  6. Nevertheless the evaluability of the choice tasks was facilitated by a number of showcards read-out by the interviewer and presented to the respondents. The verbal description of each attribute was accompanied by illustrations, including a set of maps for describing the spatial scope attribute.

  7. For example, prior to proceeding to the series of DCEs, the respondents were read and given a showcard stating: “Before making your choices, please consider:

    • Whether or not these improvements are important to you;

    • Any money you would pay towards the improvements here will not be available for you to spend on other things;

    • Other household bills may go up or down affecting the amount of money you have to spend in general; and

    • That there may be other aspects of local services that also require improvements which may increase bills.”

  8. The question wording was: “Considering a choice involving changes to all of the environmental features you have considered, would you in principle be willing to pay some amount of money per year, in terms of an increase in your council tax bill, to ensure that all of the environmental improvements were made?”.

  9. Non-linearities in the utility function are potentially important (Lanz et al. 2010; Viscusi and Huber 2012). In the present context, however, comparison of model statistics based on a non-parametric (dummy-coded) utility function suggests that a linear representation of preferences is appropriate.

  10. A more sophisticated approach proposed by Campbell et al. (2010) is to use a mixture of distributions to generate bimodal mixing distributions. While this is beyond the scope of our analysis, we note that their estimated taste distribution also implies that a fraction of respondents have a negative marginal utility of income.

  11. For respondents with zero marginal utility of income, the individual WTP is not identified, and the RUM assumptions mechanically imply an infinite WTP. In turn, this implies that the distribution of the WTP has no finite moments (Meijer and Rouwendal 2006).

  12. The list of amenities differs from the DCE attributes, breaking down the community facilities attribute (‘facilities for children and teenagers’, and other ‘outdoor facilities’) and including local nature reserves (‘nature areas’). Local nature reserves were not included as an attribute in the DCEs since they are typically not the subject of regeneration initiatives. Nevertheless their inclusion in this initial question intended to provide a comprehensive coverage of the features of the local environment in Seaham.

  13. We find that controlling for unobserved heterogeneity in tastes dramatically improves the models’ explanatory power, and we thus focus on MXL specifications. AIC/pseudo \(\text{ R }^2\) measures for MNL models are 862.0/9.2, 857.1/9.7, and 844.9/11.0 for each block respectively.

  14. The small number of choices per respondent makes the estimation of individual-level coefficients hazardous, but computations indicate that 13 respondents are best described as bearing a negative marginal utility of income in Block 1, 17 in Block 2 and 11 in Block 3, corresponding respectively to 12, 16 and 10 % of the sample. Evidence from a latent class model also suggests that a small group of respondents are best described with a negative marginal utility of income, although estimates are not statistically significantly different from zero.

  15. To further quantify this finding, we estimated a mixed logit model with a dummy-coded price variable to separately identify the valuation of each price level. Results suggest a positive but statistically insignificant effect of the smallest price level (L1). Hence on average, respondents were not sensitive to the smallest price level, and for this price preferred proposed improvements over the status quo.

  16. AIC / pseudo \(\text{ R }^2\) measures for the MXL model with fixed cost estimates are 664.4/32.2, 660.0/32.4, and 727.9/25.1 for each block respectively.

  17. Results from MXL specification with fixed cost coefficient are similar to those from MXL models with random cost coefficient, and thus not reported.

  18. Note that all respondents declared to be responsible or jointly-responsible for paying bills, but 14 (approx. 13 %) were not able to report their council tax payments. However, we find no correlation between knowledge of council tax payments and ignoring the cost attribute, suggesting that these respondents did consider the payment vehicle seriously.

  19. An alternative measure of spatial scope is the \(\text{ km }^2\) area affected by the policy change. We found this measure to have a lower explanatory power, so that we prefer the more direct measure of the population affected. The qualitative results are not affected by the choice of the units.

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Acknowledgments

We thank Mehdi Farsi, Zara Phang, Peter Tyler, Colin Warnock and Ken Willis, three anonymous reviewers, and participants at the 2011 EAERE conference for their comments and suggestions. Funding from the UK Department for Communities and Local Government is gratefully acknowledged. The views and opinions expressed in this paper do not necessarily reflect those of institutions involved. Any remaining errors are ours.

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Authors and Affiliations

  1. Chair of Integrative Risk Management and Economics and Centre for Energy Policy and Economics, ETH Zürich, Zürichbergstrasse 18, 8092 , Zurich, Switzerland

    Bruno Lanz

  2. Economics for the Environment Consultancy (eftec), London, UK

    Allan Provins

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  1. Bruno Lanz
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  2. Allan Provins
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Correspondence to Bruno Lanz.

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Lanz, B., Provins, A. Valuing Local Environmental Amenity with Discrete Choice Experiments: Spatial Scope Sensitivity and Heterogeneous Marginal Utility of Income. Environ Resource Econ 56, 105–130 (2013). https://doi.org/10.1007/s10640-013-9648-9

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