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Commit 58ef2a66 authored by nc71qaxa's avatar nc71qaxa
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wonv presi

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Scripts/MAKE_FILE.R
+ 4
− 1
View file @ 58ef2a66
rm(list=ls()) rm(list=ls())
library(tidyverse) library(tidyverse)
library(tidylog) library(tidylog)
library(apollo) library(apollo)
...@@ -103,3 +105,4 @@ source("Scripts/interaction_plots_presi.R") ...@@ -103,3 +105,4 @@ source("Scripts/interaction_plots_presi.R")
# mxl_wtp_case_a_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case A prot") # mxl_wtp_case_a_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case A prot")
# mxl_wtp_case_b_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case B prot") # mxl_wtp_case_b_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case B prot")
# mxl_wtp_case_c_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp_Case_C prot") # mxl_wtp_case_c_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp_Case_C prot")
Scripts/create_tables.R
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library(choiceTools) library(choiceTools, lib.loc = "/home/nc71qaxa/r-packages")
dir.create("Tables/mxl") dir.create("Tables/mxl")
dir.create("Tables/logit") dir.create("Tables/logit")
...@@ -90,8 +91,8 @@ texreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_mod ...@@ -90,8 +91,8 @@ texreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_mod
caption = "Results of OLS on consequentiality score.", caption = "Results of OLS on consequentiality score.",
file="Tables/ols/consequentiality.tex") file="Tables/ols/consequentiality.tex")
# Opt Out # Opt Out modified case D
texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C), texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_D, ols_opt_out_control_D),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"), custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Number of opt-out choices" = 1:4), custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
......
Scripts/interaction_plots_presi.R
+ 21
− 9
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...@@ -28,31 +28,43 @@ create_interaction_term_plot <- function(ols_summary, treatment_labels, ord, uni ...@@ -28,31 +28,43 @@ create_interaction_term_plot <- function(ols_summary, treatment_labels, ord, uni
} }
case_A_labels <- c("Treated", "Voluntary Treated") case_A_labels <- c("Treated", "Optional Treatment")
case_C_labels <- c("No Info 2", "Text 1", "Text 2", "Video 1", "Video 2") case_C_labels <- c("No Info 2", "Text 1", "Text 2", "Video 1", "Video 2")
case_C_labels_re <- c("Text 1", "Text 2", "Video 1", "Video 2", "No Info 2") case_C_labels_re <- c("Text 1", "Text 2", "Video 1", "Video 2", "No Info 2")
case_D_labels <- c("No Info", "Treated", "Vol. Treated")
case_D_labels_re <- c("Treated", "Vol. Treated", "No Info")
plot_interview_A <- create_interaction_term_plot(ols_time_spent_control_A, case_A_labels, case_A_labels, plot_interview_A <- create_interaction_term_plot(ols_time_spent_control_A, case_A_labels, case_A_labels,
"Interview Time in Seconds", -250, 380) "Interview Time in Seconds", -380, 380)
plot_interview_C <- create_interaction_term_plot(ols_time_spent_control_C, case_C_labels, case_C_labels_re, plot_interview_C <- create_interaction_term_plot(ols_time_spent_control_C, case_C_labels, case_C_labels_re,
"Interview Time in Seconds", -250, 380) "Interview Time in Seconds", -380, 380)
plot_interview_D <- create_interaction_term_plot(ols_time_spent_control_D, case_D_labels, case_D_labels_re,
"Interview Time in Seconds", -380, 380)
plot_cc_A <- create_interaction_term_plot(ols_time_cc_control_A, case_A_labels, case_A_labels, plot_cc_A <- create_interaction_term_plot(ols_time_cc_control_A, case_A_labels, case_A_labels,
"Mean Choice Card Time in Seconds", -5, 5) "Mean Choice Card Time in Seconds", -5, 5)
plot_cc_C <- create_interaction_term_plot(ols_time_cc_control_C, case_C_labels, case_C_labels_re, plot_cc_C <- create_interaction_term_plot(ols_time_cc_control_C, case_C_labels, case_C_labels_re,
"Mean Choice Card Time in Seconds", -5, 5) "Mean Choice Card Time in Seconds", -5, 5)
plot_cc_D <- create_interaction_term_plot(ols_time_cc_control_D, case_D_labels, case_D_labels_re,
"Mean Choice Card Time in Seconds", -5, 5)
plot_mani_A <- create_interaction_term_plot(ols_percentage_correct_control_A, case_A_labels, case_A_labels, plot_mani_A <- create_interaction_term_plot(ols_percentage_correct_control_A, case_A_labels, case_A_labels,
"Percentage of Correct Quiz Statements", -5, 15) "Percentage of Correct Quiz Statements", -15, 15)
plot_mani_C <- create_interaction_term_plot(ols_percentage_correct_control_C, case_C_labels, case_C_labels_re, plot_mani_C <- create_interaction_term_plot(ols_percentage_correct_control_C, case_C_labels, case_C_labels_re,
"Percentage of Correct Quiz Statements", -5, 15) "Percentage of Correct Quiz Statements", -15, 15)
plot_mani_D <- create_interaction_term_plot(ols_percentage_correct_control_D, case_D_labels, case_D_labels_re,
"Percentage of Correct Quiz Statements", -15, 15)
plot_cons_A <- create_interaction_term_plot(conseq_model_control_A, case_A_labels, case_A_labels, plot_cons_A <- create_interaction_term_plot(conseq_model_control_A, case_A_labels, case_A_labels,
"Consequentiality Score", -0.5, 0.8) "Consequentiality Score", -0.8, 0.8)
plot_cons_C <- create_interaction_term_plot(conseq_model_control_C, case_C_labels, case_C_labels_re, plot_cons_C <- create_interaction_term_plot(conseq_model_control_C, case_C_labels, case_C_labels_re,
"Consequentiality Score", -0.5, 0.8) "Consequentiality Score", -0.8, 0.8)
plot_cons_D <- create_interaction_term_plot(conseq_model_control_D, case_D_labels, case_D_labels_re,
"Consequentiality Score", -0.8, 0.8)
plot_opt_A <- create_interaction_term_plot(ols_opt_out_control_A, case_A_labels, case_A_labels, plot_opt_A <- create_interaction_term_plot(ols_opt_out_control_A, case_A_labels, case_A_labels,
"Number of Status Quo Choices", -1.5, 1) "Number of Status Quo Choices", -1.5, 1.5)
plot_opt_C <- create_interaction_term_plot(ols_opt_out_control_C, case_C_labels, case_C_labels_re, plot_opt_C <- create_interaction_term_plot(ols_opt_out_control_C, case_C_labels, case_C_labels_re,
"Number of Status Quo Choices", -1.5, 1) "Number of Status Quo Choices", -1.5, 1.5)
project_start.qmd
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--- ---
title: "Hot Cities, Cool Choices?" title: "Hot Cities, Cool Choices:"
subtitle: "The Effect of Voluntary and Obligatory Information on Preferences for Urban Green Spaces" subtitle: "The Effect of Optional and Obligatory Information on Stated Preferences for Urban Green Spaces"
title-slide-attributes: title-slide-attributes:
data-background-image: Grafics/iDiv_logo_item.png data-background-image: Grafics/iDiv_logo_item.png
data-background-size: contain data-background-size: contain
...@@ -14,7 +14,7 @@ format: ...@@ -14,7 +14,7 @@ format:
slide-number: true slide-number: true
smaller: true smaller: true
logo: Grafics/iDiv_logo_item.PNG logo: Grafics/iDiv_logo_item.PNG
footer: "Hot Cities, Cool Choices?" footer: "WONV 2024: Hot Cities, Cool Choices"
scrollable: true scrollable: true
embed-resources: true embed-resources: true
--- ---
...@@ -27,13 +27,15 @@ source("Scripts/MAKE_FILE.R") ...@@ -27,13 +27,15 @@ source("Scripts/MAKE_FILE.R")
library(tidyverse) library(tidyverse)
library(apollo) library(apollo)
library(texreg) library(texreg)
list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated", list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Optional Treatment",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1", "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)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", "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") "Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
``` ```
# Motivation & Research Contribution
## Motivation (1) ## Motivation (1)
::: incremental ::: incremental
...@@ -49,13 +51,13 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = ...@@ -49,13 +51,13 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
- Too **much information** may increase survey **complexity**, leading to respondents being overburdened with it and producing less consistent choices. - 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**. - 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. - 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. - **Voluntary information** allows the respondents to gather required information if needed and might increase efficiency of information provision
::: :::
## Literature ## Literature
::: incremental ::: incremental
- There is **little research** on the effects of **voluntary information provision** on choice behavior and information recall. - There is **little research** on the effects of **optional 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. - 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. - 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. - Both studies conclude that, on average, respondents who retrieve voluntary information have **larger willingness to pay** for the good to be valued.
...@@ -65,19 +67,21 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = ...@@ -65,19 +67,21 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## Research Contribution ## Research Contribution
::: incremental ::: incremental
- Our study explores the impact of additional obligatory and voluntary information on stated preferences using an exogenous split sample approach with three treatments. - Our study explores the impact of additional obligatory and optional 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 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. - We test who choose additional information and to what extent they have different preferences than respondents who do not choose additional information.
::: :::
## Research Questions ## Research Questions
::: incremental ::: incremental
1. How do obligatory and voluntary information treatments affect **survey engagement**, **information recall**, **consequentiality**, and **stated preferences**? 1. How do obligatory and optional information provision affect **survey engagement**, **information recall**, **consequentiality**, and **stated preferences**?
2. Do **socio-demographic** or **attitudinal** variables influence the decision to **access voluntary information**? 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? 3. Do **survey engagement**, **information recall**, **consequentiality**, and **stated preferences** differ between respondents who **access voluntary information** and those who do not?
::: :::
# Survey & Data
## Discrete Choice Experiment ## Discrete Choice Experiment
::: incremental ::: incremental
...@@ -129,15 +133,15 @@ To what extent do you agree or disagree with the following statements? ...@@ -129,15 +133,15 @@ To what extent do you agree or disagree with the following statements?
## Experimental Setting ## Experimental Setting
![](Grafics/FlowChart.png){width="300"} ![](Grafics/FlowChart_4_groups.png){width="300"}
## Case A ## Case A
![](Grafics/FlowChart_A.png){width="300"} ![](Grafics/FlowChart_4_groups_A.png){width="300"}
## Case B ## Case B
![](Grafics/FlowChart_B.png){width="300"} ![](Grafics/FlowChart_4_groups_B.png){width="300"}
## Data ## Data
...@@ -154,6 +158,8 @@ To what extent do you agree or disagree with the following statements? ...@@ -154,6 +158,8 @@ To what extent do you agree or disagree with the following statements?
+ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness**. + **Attitudinal variable**: Measure derived from 21 items on **nature relatedness**.
::: :::
# Methods
## Methods (1) {auto-animate="true"} ## Methods (1) {auto-animate="true"}
- Logit regression (voluntary information access): - Logit regression (voluntary information access):
...@@ -201,229 +207,131 @@ and ...@@ -201,229 +207,131 @@ and
```{=tex} ```{=tex}
\begin{equation} \begin{equation}
v_{Treat_A} = \{Treated, Voluntary Treated\} v_{Treat_A} = \{Treated, Optional Treatment\}
\end{equation} \end{equation}
``` ```
```{=tex} ```{=tex}
\begin{equation} \begin{equation}
v_{Treat_B} = \{Text 1, Text 2, Video 1, Video 2, No Info 2\} v_{Treat_B} = \{Treated, Vol. Treated, No Info\}
\end{equation} \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", # Case A: Obligatory vs. optional information
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 <!-- ::: {style="font-size: 45%;"} -->
<!-- ```{r, results='asis'} -->
::: panel-tabset
### Table
::: {style="font-size: 55%;"} <!-- htmlreg(l=list(ols_time_spent_control_A, ols_time_cc_control_A, ols_percentage_correct_control_A, conseq_model_control_A), -->
```{r, results='asis'} <!-- custom.model.names = c("Interview Time", "CC Time", "Quiz", "Cons. Score"), -->
htmlreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C), <!-- custom.header = list("Model 1A" = 1:1, "Model 2A" = 2:2, "Model 3A" = 3:3, "Model 4A" = 4:4), -->
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"), <!-- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", -->
custom.header = list("Dependent variable: Net interview time" = 1:4), <!-- label = "tab:olsA", -->
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", <!-- caption = "Results of OLS regressions for Scenario Case A.") -->
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 ## OLS Engagement: Interview & Choice Card Time
::: panel-tabset
### Plot
```{r} ```{r}
ggpubr::ggarrange(plot_cc_A, plot_cc_C) ggpubr::ggarrange(plot_interview_A, plot_cc_A)
``` ```
### Table
::: {style="font-size: 55%;"} ## OLS: Information recall & Consequentiality
```{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} ```{r}
ggpubr::ggarrange(plot_mani_A, plot_mani_C) ggpubr::ggarrange(plot_mani_A, plot_cons_A)
``` ```
### Table
::: {style="font-size: 55%;"} ## MXL: Effects on stated preferences
::: {style="font-size: 60%;"}
```{r, results='asis'} ```{r, results='asis'}
htmlreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_percentage_correct_C, ols_percentage_correct_control_C), htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"), custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4), "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", "wd" = "Walking Distance", "asc" = "ASC SQ"),
custom.note = "%stars. Standard errors in parentheses.", custom.model.names = c("Mean", "SD", "Treated", "Optional Treatment"), custom.note = "%stars. Standard errors in parentheses.",
label = "tab:mani", single.row = TRUE, stars = c(0.01, 0.05, 0.1), float.pos="tb",
caption = "Results of OLS on percentage of correct quiz statements.") label = "tab:mxl_A",
caption = "Results of mixed logit model with treatment interactions for Case A.")
``` ```
::: :::
:::
<!-- ## 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 --> # Case B: Voluntary information access
<!-- **Only the treated participants got these questions!** --> ## Logit Regression: Who choses treatment?
## OLS: Consequentiality ::: {style="font-size: 65%;"}
```{r, results='asis'}
::: panel-tabset
### Plot
```{r} htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
ggpubr::ggarrange(plot_cons_A, plot_cons_C) 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.")
### 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 ## OLS Engagement: Interview & Choice Card Time
::: panel-tabset
### Plot
```{r} ```{r}
ggpubr::ggarrange(plot_opt_A, plot_opt_C) ggpubr::ggarrange(plot_interview_D, plot_cc_D)
```
### 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.")
``` ```
:::
:::
## OLS: Information recall & Consequentiality
## MXL: Effects on stated preferences
::: {style="font-size: 60%;"}
::: panel-tabset
### Case A
```{r, results='asis'} ```{r}
htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])), ggpubr::ggarrange(plot_mani_D, plot_cons_D)
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 ## MXL: Case B
::: {style="font-size: 60%;"} ::: {style="font-size: 60%;"}
```{r, results='asis'} ```{r, results='asis'}
htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])), htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent", custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
"ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness", "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ", "wd" = "Walking Distance", "asc" = "ASC SQ", "ASC" ="ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"), "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.", custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info", "NR-Index"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb", stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_NR", label = "tab:mxl_C_NR",
caption = "Results of mixed logit model with treatment and NR-index interactions for Case B.") caption = "Results of mixed logit model with treatment interactions for Case B.")
``` ```
::: :::
<!-- ## Case D -->
<!-- ```{r} -->
<!-- summary(case_d) -->
<!-- ``` -->
## Discussion (1) ## Discussion (1)
1. How do obligatory and voluntary information treatments affect survey engagement, information recall, consequentiality, and stated preferences? 1. How do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences?
::: incremental ::: incremental
...@@ -477,9 +385,11 @@ htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])), ...@@ -477,9 +385,11 @@ htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
- Voluntary information access is correlated with increased consequentiality, higher survey engagement and higher willingness to pay - Voluntary information access is correlated with increased consequentiality, higher survey engagement and higher willingness to pay
- Obligatory information treatment is more effective than optional - Obligatory information treatment is more effective than optional on the cost of slightly reduced survey engagement
- Voluntarily accessed treatment shows strongest effects, but is highly endogenous
- Voluntarily accessed treatment shows strongest effects - Providing optional information seem to rather increase inequality in good-specific knowledge than decreasing it
::: :::
......
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