The economic value of urban green in German cities
Creators: name: Cavallaro, Nino
name: Sagebiel, Julian
name: Marder, Fabian
Publisher: name: OSF Registries
Year: 2023
Subjects: subject: Econometrics
subject: FOS: Economics and business
subject: Economics
subject: Agricultural and Resource Economics
subject: Social and Behavioral Sciences
subject: Urban Studies and Planning
subject: Choice Experiement
subject: Urban Ecosystem Services

description: Preregistration Template Choice Experiments
 

Part A: General Information


1. Summary of the study: 

The study aims to estimate the value of urban green with different methods. A representative quota sample is taken from a panel with German adults from the fourteen biggest cities in Germany. The survey has been conducted twice in 2020 and 2022. While the survey contains three valuation methods (hedonic pricing, life satisfaction, choice experiment), this pre-registration is only for the choice experiment. The questionnaire includes several other questions on socio-demographics, housing characteristics, attitudes, values, and perceptions. 

2. What are your hypotheses?

H1: Living closer to an urban green space increases utility
H2: People prefer more natural urban green spaces to less natural ones
H3: People are willing to pay a higher rent for closer urban green spaces to their home and for more naturalness of this urban green space
H4: Preferences for distance to urban green space and naturalness differ between cities and socio-demographics of respondents
H5: Providing additional information on the effect of naturalness of urban green on the urban heat island effect increases the willingness to pay for more natural urban green spaces.
H6: Providing additional information on the effect of naturalness of urban green on the urban heat island effect does not increase the willingness to pay for for closer distances to the park.
H7: Providing additional information on the effect of naturallness of urban green on the urban heat island effect does not change the marginal utility of income.

3. What is the format of the survey instrument? [eg. online, ftf, etc.]

The survey is carried out online with an web questionnaire designed in “LimeSurvey”. The data is collected by the survey company “IMUG” from a representative panel provided by Gapfish.

4. Motivate and describe your sampling approach (Probabilistic sample, Convenience sample, stratified, clustered, quotas applied):

The sample should be representative at the country level with respect to gender, monthly household income and age. We ensure this by using quotas. The sample only consists of people who live in a rented apartment or house. People who live in their own apartment or house were screened out at the beginning of the survey.

5. How did you incentivize your sample?

Respondents receive a payment for completing the questionnaire. The payment procedure is handled by the survey company.

6. How did you determine the sample size (e.g. power analysis)?

The sample size is restricted to our budget. However, we have conducted two similar surveys with similar sample sizes before, indicating that the current sample size is sufficient to test our hypotheses. We did not conduct a power analysis.

7. Did you conduct any pre-studies to develop the DCE and questionnaire? Please describe the procedure. 

We draw from the two earlier studies. The results from the earlier studies are accepted for publication in Land Economics. The earlier studies were extensively pre-tested as described in the paper “The value of naturalness of urban green spaces: Evidence from a discrete choice experiment”:

“[...] the survey was pretested from the 15th of April 2020 to the 21st of April 2020 with 520 participants, of which 264 respondents answered the pretest completely. We used this pretest sample to assess the suitability of our survey, the comprehensibility of the questions, and to design the choice sets.”

and

“The graphical scale was originally developed by several expert panel meetings of biologists and economists to reflect an increasing index of biodiversity and ecosystem services (such as water purification and carbon sequestration). It was validated and slightly revised in the three focus group discussions, where respondents were asked among the others (i) “What do you understand by the term ‘naturalness’? (ii) “What characteristics does a near-natural green space have for you?” (iii) rank the five illustrations by the degree of naturalness.”

Bronnmann, Julia, Liebelt, Veronika, Marder, Fabian, Meya, Jasper and Quaas, Martin F., 2022 “The Value of Naturalness of Urban Green Spaces: Evidence From a Discrete Choice Experiment”, Land Economics (forthcoming) 

We have also conducted a pre-test with N=200. Based on the results, we slightly modified the questionnaire.

8. Anything else you want to mention regarding survey information:

No
 
 
Part B: Experimental Design


9. Describe the choice situation or good to be valued:

In the setting of the choice experiment, the respondents are told that they should imagine that the local city authorities are planning to restructure the individually most visited urban green space of the respondent. The restructuring affects the naturalness of the green space and the walking distance to the green space. In the choice situation, the respondents can choose between the status quo and two alternative restructuring programs, which come with additional monthly costs via the incidental rental costs. 

10. Which attributes (and respective levels) are you using?

The attributes are the naturalness of the urban green space, the walking distance to the urban green space and the monthly rent in € per month. 
The levels for naturalness are hardly natural, little natural, partly natural, nearly natural, and very natural, which are defined by a self-developed graphical scale (see attachment). 
The levels for the walking distance are based on the reported walking distance of the respondents in the survey. The levels are -50%, +50%, +100%, +200%, and +400% of the actual reported walking distance. The figures are presented in absolute values.
The levels for the monthly rent are based on the actual rent reported in the survey. The levels are -1%, -0.5%, +0.5%, +1%, +2%, and +5% of the actual rent. The figures are presented in absolute values.
Before the choice experiment the respondents were asked about the current value of the attributes. This information was used in the choice experiment. For example, if a respondent stated the current distance is 100 meters, the displayed levels in the choice cards are 50m, 150m, 200m, 300m, 500m. The survey was programmed in a way that the absolute values where calculated during survey conduction.

11. List the number of alternatives per choice set, the number of choice sets in total and any blocks or random assignments if applicable. 

There are three alternatives per choice set and 30 choice sets in total. The choice sets are randomly assigned to the respondents, such that each respondent faces ten choice sets.

12. How did you code your attributes in the design? [eg. linear, dummy, effects]
In the design, the attributes were coded linearly with the relative values. 

13. Which socio-demographic and other case-specific variables do you need to test your hypotheses? Did you integrate socio-demographic and other case-specific variables in the design process, and if yes how?

We need the treatment dummies to test H5. We need the respondent’s city and socio-demographic variables age, gender, income, household size to test H3. These variables were not included in the experimental design

14. Which assumptions on the utility function (or other decision-making rules) did you use to generate the design? [e.g. Random utility maximization, random regret minimization etc.]

We assumed a multinomial logit model with continuous attributes.
We assumed that people choose according to random utility maximization.

15. How did you create your experimental design? [orthogonal, efficient, number of choice sets per person; labelled vs. unlabeled DCE etc.]

We used the same design as in the surveys before. We used two unlabeled alternatives (“Program 1”, “Program 2”) and one status quo (“My current situation”). The design was adapted after the first pretest in 2020. The design was optimized for priors taken from a pretest from 2020 and the D-efficiency criterion.

16. If efficient design, which priors did you use, and how did you obtain them?

We use the priors from the pretesting.

17. Did you test the design, e.g. with simulation (attach file)?

No

18. Do you use an additional split sample approach (between-subject design). If yes, did you integrate the split-sample in the design?

We apply a split-sample procedure to test the influence of an information treatment. The sample is splitted into three groups, one uninformed group and two informed groups. The two informed groups differ by how the information is provided. One group is passively provided with information whereas in the other group respondents have to actively decide if they want to be provided with additional information.
In the information treatment, the respondents read an explanatory paragraph of the urban heat island effect accompanied by a graphical illustration. Another paragraph described that the urban heat island effect can be reduced by more naturalness in urban green. Finally, the respondents could voluntarily click to view a 2 minute video on the urban heat island effect. On the next page, the respondents were asked to respond to two self reference questions and two quiz questions. Additionally, we showed some true and false reasons for the heat island effect and asked respondents which of these reasons were true or false.

19. Do you randomize the order of choice sets and attributes?

We randomize the order of the choice sets but not of the attributes

20. Do you use an opt-out option or a status quo?

Yes, we use a status quo option.

21a. How do you define the status quo?

The definition of the status quo is based on the responses given by the individual participant at an earlier stage in the questionnaire. 

 21b. Are your attributes constant or adapt to the status quo?

The levels of the naturalness attribute are constant, the levels for the walking distance and the monthly rental payment adapt to the status quo. (see 12)

22. Anything else you want to mention regarding experimental design:

No


Part C: Estimation


23. Which discrete choice models do you plan to estimate? Describe your model specification (e.g. utility function to be estimated)

We rely on random utility theory and will estimate random parameters logit models, conditional logit models and latent class logit models.

23a. If you consider multiple models, specify your preferred specification or model averaging approach (e.g. but not limited to policy recommendations, welfare measures, methodical questions).

We will not do model averaging. We prefer a random parameters model as this was also used in the earlier surveys.

24. Do you control for unobserved and observed heterogeneity, if yes, how (e.g. interactions, membership function in latent class model ?

We include unobserved preference heterogeneity by estimating a mixed logit model and observed heterogeneity via interaction terms with attributes. 

25. Do you estimate separate models for different groups?
We estimate one model for each of the 14 cities where we are conducting the choice experiment and one model for all respondents. We also estimate separate models for the different treatments.

26. Do you derive any welfare measures and how?

We are interested in obtaining willingness to pay values for the attributes naturalness and walking distance.

27. If methodical research question, describe in detail how you test your methodical hypotheses.

We want to compare the effects of the different information treatments. To do so, we estimate separate models and compare coefficients with Poe and Z-tests. Further we estimate a pooled model with treatment dummy interaction terms.

28. Describe any additional exploratory analysis you have planned to conduct.
We want to include some attitudinal statements which we model with hybrid choice models. 

29. Anything else you want to register regarding the design?
 
No 
 
Part E: Validity

30. Do you use any measures to enhance validity and reduce hypothetical bias (e.g. cheap talk, consequentiality, budget reminder, quiz questions)?

We have questions on consequentiality perceptions after the choice experiment. We also have quiz questions and self reference questions for the treatments with additional information.

31. Which follow up questions do you use?

Questions on perceived consequentiality.


Part D: General


32. What Inference criteria do you use (e.g. z-test 5% level)

We use z-Tests (5% significance level).

33. Define how you deal with outliers and how you exclude respondents/choices.

We do not plan to exclude any observations from respondents who have completed the whole survey. We will not include observations from respondents who did not complete the survey.

34. How do you deal with missing data?

We will exclude respondents who have not responded to variables which we use in our models.

35. Anything else you want to preregister?

no

Resources:
Please add any resources which are relevant to understand your experiment
Screenshot of choice sets Yes
Link to questionnaire or questionnaire pdf Yes
Code for simulation No
Other resources:
Screenshot of naturalness illustrations
Ngne code to generate the design