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\title{Preregistration Template - Choice Experiments}
\author{}
\date{}
\begin{document}
\maketitle


\section*{Part A: General Information}\label{part-a-general-information}

\subsection{1. Summary of the study}\label{summary-of-the-study}

\emph{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.}

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\ldots{}

\subsection{2. What are your research questions and/or hypotheses?}\label{what-are-your-research-questions-andor-hypotheses}

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

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.

\subsection{3. What is the format of the survey instrument?}\label{what-is-the-format-of-the-survey-instrument}

\emph{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?}

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.

\subsection{4. Motivate and describe your sampling approach}\label{motivate-and-describe-your-sampling-approach}

\emph{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?}

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.

\subsection{5. How did you incentivize your sample?}\label{how-did-you-incentivize-your-sample}

\emph{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.}

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

\subsection{6. How did you determine the sample size?}\label{how-did-you-determine-the-sample-size}

\emph{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.}

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.

\subsection{7. Did you conduct any pre-studies to develop the DCE and questionnaire? Please describe the procedure.}\label{did-you-conduct-any-pre-studies-to-develop-the-dce-and-questionnaire-please-describe-the-procedure.}

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

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'': ``{[}\ldots{]} 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.

\subsection{8. Define how you deal with outliers and how you exclude respondents/choices.}\label{define-how-you-deal-with-outliers-and-how-you-exclude-respondentschoices.}

\emph{Criteria to determine and exclude outliers should be specified
before survey conduction. Criteria could be related for example to
survey time. }

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.

\subsection{9. How do you deal with missing data?}\label{how-do-you-deal-with-missing-data}

\emph{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. }

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

\subsection{10. Anything else you want to mention regarding survey information:}\label{anything-else-you-want-to-mention-regarding-survey-information}

\emph{A free text if anything important has not been captured so far.}

No additional remarks.

\section{Part B: Experimental Design}\label{part-b-experimental-design}

\subsection{11. Describe the choice situation or good to be valued}\label{describe-the-choice-situation-or-good-to-be-valued}

\emph{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
Johnston et al. (2017)}

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\ldots{}

\subsection{12. Which attributes (and respective levels) are you using?}\label{which-attributes-and-respective-levels-are-you-using}

\emph{For each attribute in your survey, provide a clear and concise
description. Also explain each level if needed.}

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.

\subsection{13. List the number of alternatives per choice set, the number of choice sets in total and any blocks or random assignments if applicable:}\label{list-the-number-of-alternatives-per-choice-set-the-number-of-choice-sets-in-total-and-any-blocks-or-random-assignments-if-applicable}

\emph{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.}

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.

\subsection{14. How did you code your attributes to generate your design?}\label{how-did-you-code-your-attributes-to-generate-your-design}

\emph{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.}

In the design, the attributes were coded linearly with the relative
values.

\subsection{15. Did you integrate socio-demographic and other case-specific variables in the design process, and if yes how?}\label{did-you-integrate-socio-demographic-and-other-case-specific-variables-in-the-design-process-and-if-yes-how}

\emph{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.}

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.

\subsection{16. Which assumptions on the utility function did you use to generate the design?}\label{which-assumptions-on-the-utility-function-did-you-use-to-generate-the-design}

\emph{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)?}

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

\subsection{17. How did you create your experimental design?}\label{how-did-you-create-your-experimental-design}

\emph{When you do your design with a specific software (NGENE,
\texttt{spdesign} in R etc.), describe all assumptions (utility
specification, optimization routine etc.). See for example Scarpa and
Rose (2008) for reporting guidelines of efficient designs.}

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'').

\subsection{18. If an efficient design, which priors did you use, and how did you obtain them?}\label{if-an-efficient-design-which-priors-did-you-use-and-how-did-you-obtain-them}

\emph{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 Bliemer
and Collins (2016) and Walker et al. (2018).}

We used the priors obtained from the pretesting phase, which involved a
detailed analysis of initial responses to refine the utility functions.

\subsection{19. Did you test the design, e.g., with simulation?}\label{did-you-test-the-design-e.g.-with-simulation}

\emph{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} \texttt{simulateDCE} (Sagebiel 2025).
\emph{If you did not use simulation, describe how you decided for the
design you choose and what other designs you have compared it to.}

No, the design was not tested with simulation but was based on
theoretical constructs and previous empirical evidence.

\subsection{20. Do you use an additional split sample approach (between-subject design)?}\label{do-you-use-an-additional-split-sample-approach-between-subject-design}

\emph{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.}

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.

\subsection{21. Do you randomize the order of choice sets, alternatives and attributes?}\label{do-you-randomize-the-order-of-choice-sets-alternatives-and-attributes}

\emph{In most cases, it is recommended to randomize the order of choice
sets, alternatives and attributes (Mariel et al. 2021, 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.}

We randomize the order of the choice sets but not the attributes to
maintain consistency across sessions.

\subsection{22. Do you use an opt-out option or a status quo?}\label{do-you-use-an-opt-out-option-or-a-status-quo}

\emph{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.}

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

\subsection{23. How do you define the status quo?}\label{how-do-you-define-the-status-quo}

\emph{The definition of the status quo is crucial for the interpretation
of the results. Note down how it is described to the respondents.}

The status quo is defined as the current situation of the respondent
regarding the attributes being studied.

\subsection{24. Are your attributes constant or adapt to the status quo?}\label{are-your-attributes-constant-or-adapt-to-the-status-quo}

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

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

\subsection{25. Anything else you want to mention regarding experimental design:}\label{anything-else-you-want-to-mention-regarding-experimental-design}

\emph{A free text if anything important has not been captured so far.}

No additional remarks.

\section{Part C: Estimation}\label{part-c-estimation}

\subsection{26. 24. Describe your estimation strategy and any software used:}\label{describe-your-estimation-strategy-and-any-software-used}

\emph{Describe generally how you estimate your models (e.g.~maximum
likelihood) and which software you will use to estimate models.}

Estimation will be performed using Stata and R, focusing on mixed logit
models to capture random parameter variability and accommodate
unobserved heterogeneity.

\subsection{27. Which inference criteria do you use?}\label{which-inference-criteria-do-you-use}

\emph{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.}

We use z-Tests at a 5\% level, employing robust standard errors to
handle potential heteroskedasticity in the data.

\subsection{28. 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.}\label{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.}

\emph{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.}

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.

\subsection{29. Do you control for unobserved and observed heterogeneity, if yes, how (e.g.~interactions, membership function in latent class model)?}\label{do-you-control-for-unobserved-and-observed-heterogeneity-if-yes-how-e.g.-interactions-membership-function-in-latent-class-model}

\emph{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 Mariel et al. (2013) or
Sagebiel (2017). }

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

\subsection{30. Do you estimate separate models for different groups?}\label{do-you-estimate-separate-models-for-different-groups}

\emph{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.}

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.

\subsection{31. Do you derive any welfare measures and how?}\label{do-you-derive-any-welfare-measures-and-how}

\emph{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.}

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

\subsection{32. If methodical research question, describe in detail how you test your methodical hypotheses.}\label{if-methodical-research-question-describe-in-detail-how-you-test-your-methodical-hypotheses.}

\emph{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.}

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.

\subsection{33. Describe any additional exploratory analysis you have planned to conduct.}\label{describe-any-additional-exploratory-analysis-you-have-planned-to-conduct.}

\emph{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.}

We want to include some attitudinal statements which we model with
hybrid choice models.

\subsection{34. Anything else you want to mention regarding estimation:}\label{anything-else-you-want-to-mention-regarding-estimation}

\emph{A free text if anything important has not been captured so far.}

No additional remarks.

\section{Part D: Validity}\label{part-d-validity}

\subsection{35. Do you use any measures to enhance validity and reduce hypothetical bias?}\label{do-you-use-any-measures-to-enhance-validity-and-reduce-hypothetical-bias}

\emph{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 Johnston et al. (2017) for best practices.}

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.

\subsection{36. Which follow up questions do you use?}\label{which-follow-up-questions-do-you-use}

\emph{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.}

We included questions on perceived consequentiality.

\subsection{37. Resources}\label{resources}

\emph{Please add any resources which are relevant to understand your
experiment}

\begin{longtable}[]{@{}ll@{}}
\toprule\noalign{}
Resource & Link/Description \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
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 & \\
\end{longtable}

\subsection{38. Referenced Literature}\label{referenced-literature}

\phantomsection\label{refs}
\begin{CSLReferences}{1}{0}
\bibitem[\citeproctext]{ref-BLIEMER2016}
Bliemer, Michiel C. J., and Andrew T. Collins. 2016. {``On Determining
Priors for the Generation of Efficient Stated Choice Experimental
Designs.''} \emph{Journal of Choice Modelling} 21: 10--14.
https://doi.org/\url{https://doi.org/10.1016/j.jocm.2016.03.001}.

\bibitem[\citeproctext]{ref-johnston2017contemporary}
Johnston, Robert J, Kevin J Boyle, Wiktor Adamowicz, Jeff Bennett, Roy
Brouwer, Trudy Ann Cameron, W Michael Hanemann, et al. 2017.
{``Contemporary Guidance for Stated Preference Studies.''} \emph{Journal
of the Association of Environmental and Resource Economists} 4 (2):
319--405.

\bibitem[\citeproctext]{ref-MARIEL2013}
Mariel, Petr, Amaya de Ayala, David Hoyos, and Sabah Abdullah. 2013.
{``Selecting Random Parameters in Discrete Choice Experiment for
Environmental Valuation: A Simulation Experiment.''} \emph{Journal of
Choice Modelling} 7: 44--57.
https://doi.org/\url{https://doi.org/10.1016/j.jocm.2013.04.008}.

\bibitem[\citeproctext]{ref-mariel2021environmental}
Mariel, Petr, David Hoyos, Jürgen Meyerhoff, Mikolaj Czajkowski, Thijs
Dekker, Klaus Glenk, Jette Bredahl Jacobsen, et al. 2021.
\emph{Environmental Valuation with Discrete Choice Experiments: Guidance
on Design, Implementation and Data Analysis}. Springer Nature.

\bibitem[\citeproctext]{ref-SAGEBIEL2017}
Sagebiel, Julian. 2017. {``Preference Heterogeneity in Energy Discrete
Choice Experiments: A Review on Methods for Model Selection.''}
\emph{Renewable and Sustainable Energy Reviews} 69: 804--11.
https://doi.org/\url{https://doi.org/10.1016/j.rser.2016.11.138}.

\bibitem[\citeproctext]{ref-simdce}
---------. 2025. \emph{simulateDCE: Simulate Data for Discrete Choice
Experiments}.

\bibitem[\citeproctext]{ref-scarpa2008design}
Scarpa, Riccardo, and John M. Rose. 2008. {``Design Efficiency for
Non-Market Valuation with Choice Modelling: How to Measure It, What to
Report and Why.''} \emph{Australian Journal of Agricultural and Resource
Economics} 52 (3): 253--82.
https://doi.org/\url{https://doi.org/10.1111/j.1467-8489.2007.00436.x}.

\bibitem[\citeproctext]{ref-walker2018d}
Walker, Joan L, Yanqiao Wang, Mikkel Thorhauge, and Moshe Ben-Akiva.
2018. {``D-Efficient or Deficient? A Robustness Analysis of Stated
Choice Experimental Designs.''} \emph{Theory and Decision} 84 (2):
215--38.

\end{CSLReferences}




\end{document}
