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---
title: "Hot Cities, Cool Choices?"
subtitle: "The Effect of Voluntary and Obligatory Information on Preferences for Urban Green Spaces"
title-slide-attributes:
data-background-image: Grafics/iDiv_logo_item.png
data-background-size: contain
data-background-opacity: "0.2"
author: "Nino Cavallaro, Fabian Marder, Julian Sagebiel"
institute: German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
date: today
date-format: long
format:
revealjs:
slide-number: true
smaller: true
logo: Grafics/iDiv_logo_item.PNG
footer: "Hot Cities, Cool Choices?"
scrollable: true
embed-resources: true
---
```{r, include=FALSE, cache=FALSE}
source("Scripts/MAKE_FILE.R")
```
```{r loadlibs, include=FALSE}
library(tidyverse)
library(apollo)
library(texreg)
list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
"as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
"as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
"Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
```
## Motivation (1)
::: incremental
- **Stated preference** methods are frequently applied in **environmental valuation** to estimate economic values of policies, goods, and services that cannot be valued otherwise.
- Stated preference methods face **validity challenges**.
- Valid value estimation requires **sufficient information** provision about the good being valued.
- Still unclear **what formats of information** and **how much information** are optimal for valid preference elicitation.
:::
## Motivation (2)
::: incremental
- Too **much information** may increase survey **complexity**, leading to respondents being overburdened with it and producing less consistent choices.
- Too **little information** may lead respondents to **not** being able to make an **informed choice**.
- Valid preference elicitation depends not only on the provision of information, but also on the **appropriate processing and recall** of the information by the respondent.
- **Voluntary information** allows the respondents to gather required information if needed.
:::
## Literature
::: incremental
- There is **little research** on the effects of **voluntary information provision** on choice behavior and information recall.
- In their study, **Tienhaara et al. (2022)** surveyed preferences for agricultural genetic resources, allowing respondents the option to access detailed information on the valued goods prior to preference elicitation.
- Similarly, **Hu et al. (2009)** offered respondents the opportunity to access voluntary information about genetic modified food before participating in a choice experiment.
- Both studies conclude that, on average, respondents who retrieve voluntary information have **larger willingness to pay** for the good to be valued.
- Their study design, however, does not allow comparing the voluntary information retrieval to a version where the additional information was shown obligatory.
:::
## Research Contribution
::: incremental
- Our study explores the impact of additional obligatory and voluntary information on stated preferences using an exogenous split sample approach with three treatments.
- We investigate the effects of information treatments on survey engagement, information recall, consequentiality, and stated preferences, similar to Welling et al. (2023), expanding our understanding of treatment effects.
- We test who choose additional information and to what extent they have different preferences than respondents who do not choose aditional information.:::
## Research Questions
::: incremental
1. How do obligatory and voluntary information treatments affect **survey engagement**, **information recall**, **consequentiality**, and **stated preferences**?
2. Do **socio-demographic** or **attitudinal** variables influence the decision to **access voluntary information**?
3. Do **survey engagement**, **information recall**, **consequentiality**, and **stated preferences** differ between respondents who **access voluntary information** and those who do not?
:::
## Discrete Choice Experiment
::: incremental
- To investigate the research questions, we use data from a **discrete choice experiment (DCE)** on naturalness of urban green spaces.
- The survey is an exact **replication** of the choice experiment of **Bronnmann et al., (2023)** and differs only in the information provided to the respondents.
- In the DCE, respondents were asked to imagine possible **changes** to their **most frequently used UGS**.
- This **restructuring** involved adjustments to the UGS's **naturalness** and changes to the **walking distance**.
- The associated **costs** of this restructuring were intended to be integrated into monthly **rental payments**.
- Participants in the DCE were presented **ten** randomly assigned **choice cards** with a choice between **two alternative programs** for the renovation of the UGS and the **current status quo**.
:::
## Choice Card
{width="300"}
## Treatment (Information provision)
- Short info text about the effect of **natural urban green spaces** on urban **heat islands**.
- **Optional video** with the almost the same information.
{width="200"}
## Treatment (Quiz)
::: incremental
**Seven quiz questions** with strict reference to the previously provided information.
Example Questions:
1. Which of the following statements are correct?
- The temperature difference between the city and the surrounding area can be up to 10 degrees Celsius. (true/false)
2. According to the information provided, which of the following properties influences the temperature in the city?
- The proximity of green spaces to nature (yes/no)
- Light pollution in the city (yes/no)
:::
## Treatment (Self reference)
::: incremental
To what extent do you agree or disagree with the following statements?
1. I am limited by high temperatures in the city during the summer. (Strongly agree - Strongly disagree)
2. The city should do more to avoid heat islands. (Strongly agree - Strongly disagree)
:::
## Experimental Setting
{width="300"}
## Case A
{width="300"}
## Case B
{width="300"}
## Data
::: incremental
+ Quiz: Evaluation of the quiz we gave to everyone after the DCE.**-\>Information recall**
+ Timings: We saved the net interview time and the mean Choice Card time.-\> **Survey engagement**
+ **Consequentiality**:
1. To what extent do you believe that the decisions you make will have an impact on how the green spaces in your neighborhood are designed in the future? (I believe in it very much - I don’t believe in it at all )
2. To what extent do you believe that the decisions you make will affect whether you have to pay a contribution for urban greening in the future? (I believe in it very much - I don’t believe in it at all )
+ **Socio-demographics**: Age, Gender, Income, Education.
+ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness**.
:::
## Methods (1) {auto-animate="true"}
- Logit regression (voluntary information access):
```{=tex}
\begin{equation}
Y = \beta_0 + \beta_{Control} \cdot v_{Control} + \epsilon
\label{simple_logit}
\end{equation}
```
- OLS regression (survey engagement,information recall, consequentiality, status quo choices):
```{=tex}
\begin{equation}
Y = \beta_0 + \beta_{Treat} \cdot v_{Treat} + \beta_{Control} \cdot v_{Control} + \epsilon
\label{ols}
\end{equation}
```
- Mixed logit model with interactions in WTP space:
```{=tex}
\begin{equation}
U_i = -(\beta_{C_i} + \beta_{TreatC_i} \cdot v_{Treat}) \cdot (\beta_{X_i} \cdot v_{X_i} + \beta_{TreatX_i} \cdot v_{X_i} \cdot v_{Treat} - C_i) + \epsilon_i
\label{mxl_base}
\end{equation}
```
## Methods (2) {auto-animate="true"}
- Mixed logit model with interactions in WTP space:
```{=tex}
\begin{equation}
U_i = -(\beta_{C_i} + \beta_{TreatC_i} \cdot v_{Treat}) \cdot (\beta_{X_i} \cdot v_{X_i} + \beta_{TreatX_i} \cdot v_{X_i} \cdot v_{Treat} - C_i) + \epsilon_i
\end{equation}
```
with
```{=tex}
\begin{equation}
v_{X_i} = \{ASC_{sq_i}, Nat_i, WD_i\}
\end{equation}
```
and
```{=tex}
\begin{equation}
v_{Treat_A} = \{Treated, Voluntary Treated\}
\end{equation}
```
```{=tex}
\begin{equation}
v_{Treat_B} = \{Text 1, Text 2, Video 1, Video 2, No Info 2\}\end{equation}
```
## Logit Regression: Who choses treatment?
::: {style="font-size: 65%;"}
```{r, results='asis'}
htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.model.names = c("Logit regression"),
custom.header = list("Dependent variable: Voluntary Information Access" = 1),
custom.coef.map = list_ols, custom.note = "%stars. Standard errors in parentheses.",
label = "tab:logit_vt", single.row = TRUE,
caption = "Results of logit regression on access of optional information.")
```
:::
## OLS Engagement: Interview time
::: panel-tabset
### Table
::: {style="font-size: 55%;"}
```{r, results='asis'}
htmlreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Net interview time" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:net_int", single.row = TRUE,
caption = "Results of OLS on net interview time.")
```
:::
### Plot
```{r}
ggpubr::ggarrange(plot_interview_A, plot_interview_C)
```
:::
## OLS Engagement: Choice Card Time
::: panel-tabset
### Plot
```{r}
ggpubr::ggarrange(plot_cc_A, plot_cc_C)
```
### Table
::: {style="font-size: 55%;"}
```{r, results='asis'}
htmlreg(l=list(ols_time_cc_A, ols_time_cc_control_A, ols_time_cc_C, ols_time_cc_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Mean choice card time" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:cctime", single.row = TRUE,
caption = "Results of OLS on mean choice card time.")
```
:::
:::
## OLS: Information recall
::: panel-tabset
### Plot
```{r}
ggpubr::ggarrange(plot_mani_A, plot_mani_C)
```
### Table
::: {style="font-size: 55%;"}
```{r, results='asis'}
htmlreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_percentage_correct_C, ols_percentage_correct_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:mani", single.row = TRUE,
caption = "Results of OLS on percentage of correct quiz statements.")
```
:::
:::
<!-- ## Self Reference -->
<!-- 1. Es entspricht meiner persönlichen Erfahrung, dass die Grünfläche in meiner Nähe zu einem angenehmen Klima an meinem Wohnort beiträgt. -->
<!-- 2. Ich bin durch hohe Temperaturen in der Stadt im Sommer eingeschränkt. -->
<!-- 3. Die Stadt sollte mehr unternehmen, um Hitzeinseln zu vermeiden. -->
<!-- Stimme voll und ganz zu - Stimme gar nicht zu -->
<!-- **Only the treated participants got these questions!** -->
## OLS: Consequentiality
::: panel-tabset
### Plot
```{r}
ggpubr::ggarrange(plot_cons_A, plot_cons_C)
```
### Table
::: {style="font-size: 55%;"}
```{r, results='asis'}
htmlreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_model_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Consequentiality score" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:conseq", single.row = TRUE,
caption = "Results of OLS on consequentiality score.")
```
:::
:::
## OLS: Status quo
::: panel-tabset
### Plot
```{r}
ggpubr::ggarrange(plot_opt_A, plot_opt_C)
```
### Table
::: {style="font-size: 60%;"}
```{r, results='asis'}
htmlreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Number of status quo choices" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:optout", single.row = TRUE,
caption = "Results of OLS on number of status quo choices.")
```
:::
:::
## MXL: Effects on stated preferences
::: {style="font-size: 60%;"}
::: panel-tabset
### Case A
```{r, results='asis'}
htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
"ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ"),
custom.model.names = c("Mean", "SD", "Treated", "Voluntary Treated"), custom.note = "%stars. Standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_A",
caption = "Results of mixed logit model with treatment interactions for Case A.")
```
### Case B
```{r, results='asis'}
htmlreg(c(case_C_cols[1], remGOF(case_C_cols[2:7])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
"ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info"), custom.note = "%stars. Standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_C",
caption = "Results of mixed logit model with treatment interactions for Case B.")
```
:::
:::
## MXL: WTP space with NR index
::: {style="font-size: 60%;"}
```{r, results='asis'}
htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
"ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info", "NR"), custom.note = "%stars. Standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_NR",
caption = "Results of mixed logit model with treatment and NR-index interactions for Case B.")
```
:::
<!-- ## Case D -->
<!-- ```{r} -->
<!-- summary(case_d) -->
<!-- ``` -->
## Discussion (1)
1. How do obligatory and voluntary information treatments affect survey engagement, information recall, consequentiality, and stated preferences?
::: incremental
- Obligatory and voluntary treatments do not increase survey engagement measured via time spend on the survey
- Small negative effect for obligatory treatment on survey engagement
- Both treatments increase information recall, stronger effect for obligatory treatment
- No effect on consequentiality
- Strong effects on stated preferences for both treatments, more pronounced effect for the obligatory treatment
:::
## Discussion (2)
2. Do socio-demographic variables or natural connectedness influence the decision to access voluntary information?
::: incremental
- Respondents that voluntary access information are younger, richer and have a higher natural relatedness index
- No effects of gender and education
- Respondents' preferences for the good to be valued influence the likelihood of accessing additional information
:::
## Discussion (3)
3. Do survey engagement, information recall, consequentiality, and stated preferences differ between respondents who access voluntary information and those who do not?
::: incremental
- Respondents that voluntary access information do engage more in the survey & have a higher consequentiality score
- Voluntary information access is negatively correlated with number of status quo choices
- Higher willingness to pay values in groups that voluntary access information
:::
## Conclusion
::: incremental
- Obligatory and voluntary information treatments increase information recall and willingness to pay for naturalness of and proximity to urban green spaces
- Exogenous treatments do not affect consequentiality
- Voluntary information access is correlated with increased consequentiality, higher survey engagement and higher willingness to pay
- Obligatory information treatment is more effective than optional
- Voluntarily accessed treatment shows strongest effects
:::
## Appendix
Information provision (Video) Link to the video: https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4
## Summary Statistics A
{width="300"}
## Summary Statistics B
{width="300"}
<!-- ## Socio Demografics {.smaller} -->
<!-- ::: {style="font-size: 50%;"} -->
<!-- ::: panel-tabset -->
<!-- ### Case A -->
<!-- ```{r} -->
<!-- kableExtra::kable(treatment_socio_A) -->
<!-- ``` -->
<!-- ### Case B -->
<!-- ```{r} -->
<!-- kableExtra::kable(treatment_socio_C) -->
<!-- ``` -->
<!-- ::: -->
<!-- ::: -->
## NR OLS
::: {style="font-size: 65%;"}
```{r, results='asis'}
htmlreg(l=list(nr_model_treat_A), single.row = TRUE,
custom.model.names = c("OLS regression"),
custom.header = list("Dependent variable: NR-Index" = 1),
custom.coef.map = list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
"as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
"as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
"Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree", "Kids_Dummy" = "Children",
"Naturalness_SQ" = "Naturalness SQ", "WalkingDistance_SQ" = "Walking Distance SQ"),
stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:nr_ols",
caption = "Results of OLS on the NR-index.")
```
:::
## MXL: Split Samples
```{r}
ggplot(data=mxl_melt_info, aes(x=Coefficent, y=abs(value), fill=variable)) +
geom_bar(stat="identity", position='dodge', width = 0.9) +
geom_errorbar(aes(x=Coefficent, ymin=abs(value)-ME, ymax=abs(value)+ME), width=0.3, position=position_dodge(0.8)) +
ylab("Absolute Value") +
xlab("Coefficient") +
scale_x_discrete(guide = guide_axis(angle = 45)) +
scale_fill_brewer(palette = "Set2", labels = c("Treated", "Optional Treatment", "Not Treated"), name="Treatment") +
theme(legend.position = c(0.85, 0.8))
```