---
title: "Preregistration Template - Choice Experiments"
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bibliography: references.bib

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profiles:
  answers:
    metadata:
      title: "Preregistration Template - Choice Experiments with Answers"
---

# Part A: General Information {.unnumbered .unlisted}

## Summary of the study 

*Provide a summary of the whole study/survey and provide details that are relevant to understand the background of the study and survey in general. This can be extensive if necessary but should be kept brief.*

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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 fourteenth 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...
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## What are your research questions and/or hypotheses?

*Briefly but precisely state all research questions and hypotheses of this study. Hypotheses should be testable with the methods described later.*

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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 closer distances to the park.\
H7: Providing additional information on the effect of naturalness of urban green on the urban heat island effect does not change the marginal utility of income.
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## What is the format of the survey instrument?

*Elaborate on the survey instrument. Subquestions could be: Is it online or offline? Is the survey programmed by you or by an external company? Who is collecting the data? How long is the questionnaire?*

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The survey is carried out online with a web questionnaire designed in “LimeSurvey”. The data is collected by the survey company “IMUG” from a representative panel provided by Gapfish.
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## Motivate and describe your sampling approach

*Describe your sampling strategy in detail and provide reasons for it. If it was not a random sample, why not? Are you using quotas to achieve a representative sample? Who is your target population? Do you use screening questions?*

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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.
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## How did you incentivize your sample?

*Describe how respondents are compensated for completing the questionnaire. If you use individual incentives (e.g. as in a real choice experiment), explain how it works.*

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Respondents receive a payment for completing the questionnaire. The payment procedure is handled by the survey company.
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## How did you determine the sample size?

*Describe any power calculations you conducted or any other strategy you used to determine your sample size. Many surveys are restricted by a budget, which could also be mentioned here as a reason. However, a power calculation is recommended even in this case.*

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

*Describe the procedure to develop your questionnaire. This could include focus groups, expert interviews, a research project etc.*

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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 (2023)  	



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

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

*Criteria to determine and exclude outliers should be specified before survey conduction. Criteria could be related for example to survey time. *

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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.

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## How do you deal with missing data?

*In surveys, some data points may be missing, e.g. if respondents do not answer a specific question. One can exclude such respondents or use imputation methods. Explain your strategy. *

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

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## Anything else you want to mention regarding survey information:

*A free text if anything important has not been captured so far.*

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No additional remarks.
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# Part B: Experimental Design

## Describe the choice situation or good to be valued

*Provide a detailed explanation of the choice situation. What is the good to be valued. How is the scope and time frame defined. For environmental economics applications, describe the extent of the proposed change and the extent of the market. As a good orientation, see @johnston2017contemporary*

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

*For each attribute in your survey, provide a clear and concise description. Also explain each level if needed.*

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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.

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

*You can use a table here. In cases that the number of alternatives varies, or that you use different split samples for different attributes, elaborate this here.*

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There are three alternatives per choice set and 30 choice sets in total. The choice sets are randomly assigned to the respondents, ensuring that each respondent faces ten choice sets.
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##  How did you code your attributes to generate your design?

*This is specific for your design. Which coding did you use to generate your design. Examples are dummy coding, linear, relative to a status quo, etc.*

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In the design, the attributes were coded linearly with the relative values. 
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##  Did you integrate socio-demographic and other case-specific variables in the design process, and if yes how?


*Case-specific variables are variables that do not vary between alternatives but vary across respondents. These variables could include all variables that you plan to integrate into your utility function, including dummies for split samples. If not applicable, leave blank.*

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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.
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## Which assumptions on the utility function did you use to generate the design?

*Do you use a utility maximization framework? How do you specify interaction variables? Do you use non-linear specifications of some attributes (e.g. logarithm, quadratic)?*

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We assumed a multinomial logit model with continuous attributes. We assumed that people choose according to random utility maximization.
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## How did you create your experimental design?

*When you do your design with a specific software (NGENE, `spdesign` in R etc.), describe all assumptions (utility specification, optimization routine etc.). See for example @scarpa2008design for reporting guidelines of efficient designs.*

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The design used was orthogonal, and it was based on priors taken from a previous study conducted in 2020. The design included two unlabeled alternatives ("Program 1", "Program 2") and one status quo ("My current situation").
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## If an efficient design, which priors did you use, and how did you obtain them?

*Some researchers use small priors indicating the sign of the parameters, others use priors based on assumptions or previous studies. Be concise and transparent which ones you use. See for example @BLIEMER2016 and @walker2018d.*

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We used the priors obtained from the pretesting phase, which involved a detailed analysis of initial responses to refine the utility functions.
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## Did you test the design, e.g., with simulation?

*Monte-Carlo simulations are a good way to test the design for unbiasedness, power and efficiency, and can help you to justify your design choice. In R you can use* `simulateDCE` [@simdce]. *If you did not use simulation, describe how you decided for the design you choose and what other designs you have compared it to.*

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No, the design was not tested with simulation but was based on theoretical constructs and previous empirical evidence.
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## Do you use an additional split sample approach (between-subject design)?

*Split-sample approaches are frequently used to test methodical research questions or to randomize undesired effects. If you have different versions of your questionnaire that can influence the estimated parameters, explain them and why you used them.*

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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.

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## Do you randomize the order of choice sets, alternatives and attributes?

*In most cases, it is recommended to randomize the order of choice sets, alternatives and attributes [@mariel2021environmental, pp.40 and 94] to avoid undesired effects (e.g. left right bias). Note down if you do this, or if you do not do this, explain why not.*

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We randomize the order of the choice sets but not the attributes to maintain consistency across sessions.
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## Do you use an opt-out option or a status quo?

*State if you use an opt-out or status quo alternative as an alternative is your choice set. If you do not use any, you can state that you use a forced choice experiment.*

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Yes, a status quo option is used, defined based on the responses given by the individual participant at an earlier stage in the questionnaire.
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## How do you define the status quo?

*The definition of the status quo is crucial for the interpretation of the results. Note down how it is described to the respondents.*

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The status quo is defined as the current situation of the respondent regarding the attributes being studied.
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## Are your attributes constant or adapt to the status quo?

*If the status quo is constant for all respondents describe where you got the constant values from. If it varies between respondents, describe how.*

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The levels of the naturalness attribute are constant, while the levels for walking distance and monthly rental payment adapt to the status quo.
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## Anything else you want to mention regarding experimental design:

*A free text if anything important has not been captured so far.*

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No additional remarks.
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# Part C: Estimation

## 24. Describe your estimation strategy and any software used:

*Describe generally how you estimate your models (e.g. maximum likelihood) and which software you will use to estimate models.*

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Estimation will be performed using Stata and R, focusing on mixed logit models to capture random parameter variability and accommodate unobserved heterogeneity.
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## Which inference criteria do you use?

*Which tests do you apply to test your hypotheses and which significance level do you use to decide on your conclusions. If you do not use formal testing or different testing approaches, describe them.*

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We use z-Tests at a 5% level, employing robust standard errors to handle potential heteroskedasticity in the data.
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## Which discrete choice models do you plan to estimate? If you consider multiple models, specify how you select a model or if a model averaging approach will be used.

*Here, you can list all models that you plan to estimate. Often, the final model choice is an empirical question, and it is totally fine to leave it open which model you will finally use. However, you can still state which models, and which specifications, you estimate and compare to each other.*

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We rely on random utility theory and will estimate random parameters logit models, conditional logit models, and latent class logit models. We select models based on out of sample predictions and measures of fit. If applicable, we use likelihood-ratio tests to compare models. 
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## Do you control for unobserved and observed heterogeneity, if yes, how (e.g. interactions, membership function in latent class model)?

*Most applications control for  unobserved heterogeneity via mixed logit or latent class models and for observed heterogeneity via interaction terms between attributes and alternative-specific constants and case-specific variables such as age or gender. List how you plan to deal with heterogeneity. Guidance on selecting the right model for unobserved heterogeneity can be found in @MARIEL2013 or @SAGEBIEL2017. *

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We include unobserved preference heterogeneity by estimating a mixed logit model and observed heterogeneity via interaction terms with attributes.
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## Do you estimate separate models for different groups?

*To compare different groups, one can either use interaction terms or estimate separate models for each group. Elaborate if you want to estimate separate models and how you want to compare estimates between models.*

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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.
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## Do you derive any welfare measures and how?

*Welfare measures can be compensated or equivalent measures, willingness to pay or others. Explain which ones you want to use and how you calculate them.*

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We are interested in obtaining willingness to pay values for the attributes naturalness and walking distance
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## If methodical research question, describe in detail how you test your methodical hypotheses.

*There are different approaches to approach methodical research questions. Often, researchers use split samples and then compare results between them using separate models or interaction terms. Explain how you approach your methodical research question and how you plan to test it.*

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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.
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## Describe any additional exploratory analysis you have planned to conduct.

*Sometimes, you have already some other ideas on what to do with the data but are not specific about it. Here, you can elaborate on this.*

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We want to include some attitudinal statements which we model with hybrid choice models.
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## Anything else you want to mention regarding estimation:

*A free text if anything important has not been captured so far.*

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No additional remarks.
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# Part D: Validity

## Do you use any measures to enhance validity and reduce hypothetical bias?

*The stated preferences literature suffers from validity issues and hypothetical bias and other biases. Several measures have been developed to counteract these biases. Explain in detail which ones you use and if not, why not. See @johnston2017contemporary for best practices.*

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We include questions on consequentiality perceptions after the choice experiment, employ cheap talk scripts, and conduct ex-ante and ex-post validations of respondent understanding.
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## Which follow up questions do you use?

*To ex-post assess the validity of your results, you can use follow up questions, e.g. on consequentiality, comprehension and credibility, how the respondent made their choices, which attributes were perceived etc.*

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We included questions on perceived consequentiality.
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## Resources

*Please add any resources which are relevant to understand your experiment*

| Resource                          | Link/Description |
|------------------------------------|-----------------|
| Screenshot of choice sets         | [Add Link]      |
| Link to questionnaire or PDF      | [Add Link]      |
| Code for simulation               | [Add Link]      |
| Additional resource 1             |                 |
| Additional resource 2             |                 |
| Additional resource 3             |                 |

## Referenced Literature


<!-- ##  Describe how attributes were developed and tested: -->

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<!-- The attributes selected for the choice experiment were based on literature review and expert consultations to ensure relevance and comprehensibility. -->
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<!-- ##  Explain any preliminary tests or pilot studies conducted: -->

<!-- *As attribute selection is a critical step in survey development, a proper way of testing attributes and levels is required. Explain this process here.* -->

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<!-- Pilot studies were conducted to refine the survey instruments and choice sets based on respondent feedback and preliminary analysis. -->
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