150th eaae seminar: the spatial dimension in analysing the linkages between agriculture, rural...

150th EAAE Seminar:The spatial dimension in analysing the linkages between

agriculture, rural development and the environment

Edinburgh, 22 Oct 2015Julian Sagebiel

IÖW – Institute for Ecological Economy Reseach, Berlin

A simple method to account for spatially-different preferences in discrete choice experiments

Julian Sagebiel, Klaus Glenk, Jürgen Meyerhoff

Aim of this presentation

– Present an easy approach to attain spatially different willingness to pay from discrete choice experiments

– Exemplify it with data from a discrete choice experiment (DCE) on local land use changes

– Focus is on forest as alternative to agriculture

– Establish willingness to pay function to predict values on different spatial units

– Function depends on spatial variables

– Related to Benefit Transfer methods (Rolfe et al. 2015)

– Map WTP for German counties

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Introduction: Need for valuation

– Land use conflicts and need for land use changes

– Interaction with climate change

– New political perspective on nature conversation

– E.g. Convention on Biological Diversity, European Water Framework Directive

– Sustainable land use requires incorporation of all costs and benefits

– On farm level

– Climate effects

– Societal effects e.g. landscape, aesthetic value

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Introduction: Discrete choice experiments

– DCEs to inform policy decisions

– Several studies on land use conducted in Europe (van Zanten et al. 2014)

– Can be integrated into cost-benefit analysis

– Method to elicit willingness to pay (WTP) for non-market goods

– Survey based

– Respondents choose among alternative land use scenarios

– Alternatives vary in their attributes

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Introduction: Willingness to pay in space

– Willingness to pay varies in space

– People have different preferences

– People have different reference points

– Several attempts to integrate space in WTP estimates

– Kriging (Campbell 2009, Czajkowski 2015)

– Global and Local Hotspots (Campbell 2008, Johnston & Ramachandran 2013, Meyerhoff 2013)

The survey

– March/April 2013

– About 1,400 randomly sampled respondents all over Germany

– Online questionnaire of about 30 minutes

– Socio-demographics

– Land use and climate change: attitudes, perceptions, knowledge

– Recreational activities

– Each respondent revealed his place of residence on a map (WGS84)

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Spatial distribution of sample

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The discrete choice experiment

– Local land use changes

– Within 15 kilometre radius of place of residence

– Each respondent has a unique status quo situation

– 27 choice sets in three blocks

– D-efficient design for multinomial logit model

– Minimize willingness to pay standard error

– Three alternatives of which one is status quo (“as today”)

– Six land-use related attributes

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Attributes and levels

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Attribute Levels

Share of forest (ShFor) As today, decrease by 10%, increase by 10%

Field size (FiSiz) As today, half the size, twice the size

Biodiversity in agrarian landscapes (Biodiv)

As today, slight increase (85 points), considerable increase (105 points)

Share of maize on arable land (ShMai)

As today, max. 30% of fields, max. 70% of fields

Share of grassland on agricultural fields (ShGra)

As today, 25%, 50%

Annual contribution to fund (Price)

0, 10, 25, 50, 80, 110, 160 €

The approach in a nutshell

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A

B

C

D

Step A: Data preparation

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A

B

C

D

Step A: Data preparation

– Calculate within the 15 km radius the status quo of all relevant attributes (here forest share)

– Any GIS software (ArcGIS, QGIS)

– Requires land use data

– Incorporate status quo of respondent e.g. by substituting attribute level "as today" with the status quo situation

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Attribute level Original Coding SQ Modification

Status quo is 25% 85% 25% 85%

As today 0 0 25 85

10% less 1 1 15 75

10% more 2 2 35 95

Step A: Data preparation

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Attribute level Original Coding SQ Modification

Status quo is 25% 85% 25% 85%

As today 0 0 25 85

10% less 1 1 15 75

10% more 2 2 35 95

Step B: Model estimation

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A

B

C

D

Step B: Model estimation

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Estimation Results: Coefficients

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  Coefficient Standard error

ASCsq 0.18* 0.049

ShFor 0.073* 0.0040

ShForSquared -0.00064* 0.000084

FiSiz: Half -0.24* 0.044

FiSiz: Double -0.21* 0.038

Biodiv 0.18* 0.020

ShMai 0.0080* 0.0030

ShMaiSquared -0.00020* 0.000037

ShGra 0.017* 0.0038

ShGraSquared -0.00033* 0.000067

Price -0.0067* 0.00040

* p < 0.01

Step B: Model estimation

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Quadratic Utility Function

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Marginal willingness to pay

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Step C: WTP prediction

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A

B

C

D

Step C: WTP prediction

– For each county, calculate a (discrete) distribution of forest share for the population

– Requires high resolution data on population

– Here 250x250m raster data

– Similar to step A, calculate for each raster cell the share of forest

– For each county, draw a distribution of forest share

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Example Distribution of Forest Share

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Step C: WTP prediction

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Step C: WTP estimation

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Step C: Value mapping

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A

B

C

D

Step D: Value mapping

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– Map the average MWTP values

– Multiply the willingness to pay with the number of eligible inhabitants and create a map with these aggregate values

– Map further statistics, e.g. the standard deviation of willingness to pay in each county

Share of forest and marginal willingness to pay

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Results

– In general, higher MWTP where forest share is low

– But: Population density is very important

– MWTP hotspots are in the north of Germany

– Eastern Midlands are already equipped with large forests

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Discussion

Advantages

– Straightforward estimation

– Prediction on arbitrary scales

– Inclusion of several exogenous variables possible

– Flexible utility functions possible

Limitations

– Data requirement

– Unobserved heterogeneity

– Accuracy?

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References

– Campbell D, Scarpa R, Hutchinson W. Assessing the spatial dependence of welfare estimates obtained from discrete choice experiments. Letters in Spatial and Resource Sciences 2008;1:117–26. doi:10.1007/s12076-008-0012-6.

– Campbell D, Hutchinson WG, Scarpa R. Using Choice Experiments to Explore the Spatial Distribution of Willingness to Pay for Rural Landscape Improvements. Environment and Planning - Part A 2009;41:97–111. doi:10.1068/a4038.

– Czajkowski M, Budziński W, Campbell D, Giergiczny M, Hanley N, others. Spatial heterogeneity of willingness to pay for forest management. 2015.

– Johnston RJ, Ramachandran M. Modeling Spatial Patchiness and Hot Spots in Stated Preference Willingness to Pay. Environ Resource Econ 2013;59:363–87. doi:10.1007/s10640-013-9731-2.

– Meyerhoff J. Do turbines in the vicinity of respondents’ residences influence choices among programmes for future wind power generation? Journal of Choice Modelling 2013;7:58–71. doi:10.1016/j.jocm.2013.04.010.

– Rolfe J, Windle J, Bennett J. Benefit Transfer: Insights from Choice Experiments. In: Johnston RJ, Rolfe J, Rosenberger RS, Brouwer R, editors. Benefit Transfer of Environmental and Resource Values, Springer Netherlands; 2015, p. 191–208.

– van Zanten, B. T., Verburg, P. H., Koetse, M. J., and van Beukering, P. J. H. 2014. Preferences for European agrarian landscapes: A meta-analysis of case studies. Landscape and Urban Planning, 132: 89–101.

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Thank you for your time and attention

Julian SagebielIÖW – Institute for Ecological

Economy Research, Berlinjulian.sagebiel@ioew.de

Klaus GlenkSRUC, Edinburgh

klaus.glenk@sruc.ac.uk

Jürgen MeyerhoffTechnical University Berlin

juergen.meyerhoff@tu-berlin.de

Estimation Results: Statistics

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Observations 33291

Pseudo R2 0.103

AIC 21890.5

BIC 21983.1

Chi Squared 2514.1

Lok-Lik. (Null) -12191.3

Log-Lik. -10934.3