diff --git a/Scripts/MAKE_FILE.R b/Scripts/MAKE_FILE.R
index a165a46abbee8521fdece9f5dbd32a626aab29cc..98189b39eaa6388ce2c279650f739801e10b2b5a 100644
--- a/Scripts/MAKE_FILE.R
+++ b/Scripts/MAKE_FILE.R
@@ -1,4 +1,6 @@
rm(list=ls())
+
+
library(tidyverse)
library(tidylog)
library(apollo)
@@ -102,4 +104,5 @@ source("Scripts/interaction_plots_presi.R")
# # without protesters
# 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_c_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp_Case_C prot")
\ No newline at end of file
+# mxl_wtp_case_c_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp_Case_C prot")
+
diff --git a/Scripts/create_tables.R b/Scripts/create_tables.R
index 266d226f2f182fa92e9d83fe612bec792d40effe..b3d8e6acfcce8e43b77c13c64fb668fbf8b5ba26 100644
--- a/Scripts/create_tables.R
+++ b/Scripts/create_tables.R
@@ -1,4 +1,5 @@
-library(choiceTools)
+library(choiceTools, lib.loc = "/home/nc71qaxa/r-packages")
+
dir.create("Tables/mxl")
dir.create("Tables/logit")
@@ -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.",
file="Tables/ols/consequentiality.tex")
-# Opt Out
-texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
+# Opt Out modified case D
+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.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",
diff --git a/Scripts/interaction_plots_presi.R b/Scripts/interaction_plots_presi.R
index 967a8618aa6ea87bd8eac3d1e6cd6dd69ec75e33..0e3727323e8bae7cdc2e80309169d818cb2fdc8d 100644
--- a/Scripts/interaction_plots_presi.R
+++ b/Scripts/interaction_plots_presi.R
@@ -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_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,
- "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,
- "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,
"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,
"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,
- "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,
- "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,
- "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,
- "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,
- "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,
- "Number of Status Quo Choices", -1.5, 1)
+ "Number of Status Quo Choices", -1.5, 1.5)
diff --git a/project_start.qmd b/project_start.qmd
index c5557a0b0027c9e1fc91a8d6e09576ee464d0200..287eeacf73b7a832561d306de232e910d3190d0d 100644
--- a/project_start.qmd
+++ b/project_start.qmd
@@ -1,6 +1,6 @@
---
-title: "Hot Cities, Cool Choices?"
-subtitle: "The Effect of Voluntary and Obligatory Information on Preferences for Urban Green Spaces"
+title: "Hot Cities, Cool Choices:"
+subtitle: "The Effect of Optional and Obligatory Information on Stated Preferences for Urban Green Spaces"
title-slide-attributes:
data-background-image: Grafics/iDiv_logo_item.png
data-background-size: contain
@@ -14,7 +14,7 @@ format:
slide-number: true
smaller: true
logo: Grafics/iDiv_logo_item.PNG
- footer: "Hot Cities, Cool Choices?"
+ footer: "WONV 2024: Hot Cities, Cool Choices"
scrollable: true
embed-resources: true
---
@@ -27,13 +27,15 @@ source("Scripts/MAKE_FILE.R")
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",
+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 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 & Research Contribution
+
## Motivation (1)
::: incremental
@@ -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 **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.
+- **Voluntary information** allows the respondents to gather required information if needed and might increase efficiency of information provision
:::
## Literature
::: 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.
- 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.
@@ -65,19 +67,21 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## 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.
+- 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 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
::: 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**?
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
::: incremental
@@ -129,15 +133,15 @@ To what extent do you agree or disagree with the following statements?
## Experimental Setting
-{width="300"}
+{width="300"}
## Case A
-{width="300"}
+{width="300"}
## Case B
-{width="300"}
+{width="300"}
## Data
@@ -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**.
:::
+# Methods
+
## Methods (1) {auto-animate="true"}
- Logit regression (voluntary information access):
@@ -201,229 +207,131 @@ and
```{=tex}
\begin{equation}
- v_{Treat_A} = \{Treated, Voluntary Treated\}
+ v_{Treat_A} = \{Treated, Optional Treatment\}
\end{equation}
```
```{=tex}
\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}
```
-## 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.")
+# Case A: Obligatory vs. optional information
-```
-:::
-## OLS Engagement: Interview time
+<!-- ::: {style="font-size: 45%;"} -->
+<!-- ```{r, results='asis'} -->
-::: 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.")
-```
-:::
+<!-- htmlreg(l=list(ols_time_spent_control_A, ols_time_cc_control_A, ols_percentage_correct_control_A, conseq_model_control_A), -->
+<!-- custom.model.names = c("Interview Time", "CC Time", "Quiz", "Cons. Score"), -->
+<!-- custom.header = list("Model 1A" = 1:1, "Model 2A" = 2:2, "Model 3A" = 3:3, "Model 4A" = 4:4), -->
+<!-- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", -->
+<!-- label = "tab:olsA", -->
+<!-- caption = "Results of OLS regressions for Scenario Case A.") -->
+<!-- ``` -->
+<!-- ::: -->
-### 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}
-ggpubr::ggarrange(plot_cc_A, plot_cc_C)
+ggpubr::ggarrange(plot_interview_A, plot_cc_A)
```
-### 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 & Consequentiality
-## OLS: Information recall
-::: panel-tabset
-### Plot
```{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'}
-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.")
+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", "Optional Treatment"), 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.")
```
:::
-:::
-
-<!-- ## 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}
-ggpubr::ggarrange(plot_cons_A, plot_cons_C)
-```
+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.")
-### 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}
-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.")
+ggpubr::ggarrange(plot_interview_D, plot_cc_D)
```
-:::
-:::
+## OLS: Information recall & Consequentiality
-## 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.")
+```{r}
+ggpubr::ggarrange(plot_mani_D, plot_cons_D)
```
-:::
-:::
-## MXL: WTP space with NR index
+## MXL: Case B
::: {style="font-size: 60%;"}
```{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",
"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"),
- 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",
- label = "tab:mxl_NR",
- caption = "Results of mixed logit model with treatment and NR-index interactions for Case B.")
+ label = "tab:mxl_C_NR",
+ caption = "Results of mixed logit model with treatment 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?
+1. How do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences?
::: incremental
@@ -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
-- 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
:::