diff --git a/Scripts/MAKE_FILE.R b/Scripts/MAKE_FILE.R
index 98189b39eaa6388ce2c279650f739801e10b2b5a..dd201e5a3475b9421e8c00a235ed162501446440 100644
--- a/Scripts/MAKE_FILE.R
+++ b/Scripts/MAKE_FILE.R
@@ -67,6 +67,7 @@ mxl_wtp_case_b <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case B")
mxl_wtp_case_b_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR B")
mxl_wtp_case_c <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_C")
mxl_wtp_case_c_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_NR_Case_C")
+mxl_wtp_case_d <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_D base")
# rent interactions models
mxl_wtp_case_a_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case A Rent Int")
diff --git a/Scripts/create_tables.R b/Scripts/create_tables.R
index 9b14fa9f3a71fe1a76d02e0b12810bd5f1f2dbad..cceb70ddc0880cf762c5b7e9843e83433514ba4c 100644
--- a/Scripts/create_tables.R
+++ b/Scripts/create_tables.R
@@ -1,5 +1,5 @@
-#library(choiceTools, lib.loc = "/home/nc71qaxa/r-packages")
-library(choiceTools)
+library(choiceTools, lib.loc = "/home/nc71qaxa/r-packages")
+#library(choiceTools)
dir.create("Tables/mxl")
dir.create("Tables/logit")
@@ -131,7 +131,7 @@ texreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb
##### MXL #######
### Baseline case A
-case_A <- quicktexregapollo(mxl_wtp_case_a_rentINT)
+case_A <- quicktexregapollo(mxl_wtp_case_a)
coef_names <- case_A@coef.names
coef_names <- sub("^(mu_)(.*)(_T|_VT)$", "\\2\\3", coef_names)
@@ -141,14 +141,14 @@ case_A@coef.names <- coef_names
case_A_cols <- map(c("^mu_", "^sig_", "_T$", "_VT$"), subcoef, case_A)
texreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ custom.coef.map = list("ASC_sq" = "ASC SQ", "natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ"),
custom.model.names = c("Mean", "SD", "Treated", "Optional Treatment"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_A",
caption = "Results of mixed logit model with treatment interactions for Case A.",
- file="Tables/mxl/case_A_rent_INT.tex")
+ file="Tables/mxl/case_A.tex")
### Baseline case C
case_C <- quicktexregapollo(mxl_wtp_case_c_rentINT)
@@ -184,8 +184,8 @@ case_C_NR@coef.names <- coef_names
case_C_cols_NR <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$", "_NR$"), subcoef, case_C_NR)
texreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "ASC_sq" = "ASC SQ", "rent" = "Rent",
+ "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info", "NR"), custom.note = "%stars. Robust standard errors in parentheses.",
@@ -196,7 +196,7 @@ texreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
### New Case B
-case_B <- quicktexregapollo(mxl_wtp_case_d_rentINT)
+case_B <- quicktexregapollo(mxl_wtp_case_d)
coef_names <- case_B@coef.names
coef_names <- sub("^(mu_)(.*)(vol_treated|_treated|_no_info)$", "\\2\\3", coef_names)
@@ -207,8 +207,8 @@ case_B@coef.names <- coef_names
case_B_cols <- map(c("^mu_", "^sig_", "_treated$", "_vol_treated$","_no_info$"), subcoef, case_B)
texreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "ASC_sq" = "ASC SQ", "rent" = "Rent",
+ "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
@@ -251,3 +251,4 @@ texreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
# "mu_asc_T" = "ASC X Treated", "mu_nat_VT" = "Naturalness X Vol. Treated", "mu_wd_VT" = "Walking Distance X Vol. Treated",
# "mu_rent_VT" = "Rent X Vol. Treated", "mu_asc_VT" = "ASC X Vol. Treated"),
# stars = c(0.01, 0.05, 0.1), override.se = mxl_wtp_case_a_rentINT$robse, file="Tables/mxl/case_A_rent_INT.tex")
+
diff --git a/Scripts/data_prep.R b/Scripts/data_prep.R
index 5627a1cf170e9f75fbb5910c57f0da1aae6f28fe..90942782bd9f39bfe9cded11ab5e914f308ae3a8 100644
--- a/Scripts/data_prep.R
+++ b/Scripts/data_prep.R
@@ -1,134 +1,136 @@
-# Prepare variables that we want to use
-
-database_full <- database_full %>% rename(Gender = "Q03W123", Education = "Q06W123", HHSize = "Q41W123",
- WorkingTime = "Q44W123", Birthyear = "Q01W123", Rent_net = "Q07W123",
- Number_Kids = "Q42W123", Employment_type = "Q43W123", Conseq_UGS = "Q28W3",
- Conseq_Money = "Q29W3")
-
-
-database_full <- database_full %>% mutate(Gender = dplyr::recode(Gender, "A1" = 1, "A2" = 2, "A3"=3),
- Education = dplyr::recode(Education, "A1" = 1, "A2" = 2, "A3"=3, "A4" = 4, "A5" = 5),
- Employment_type = dplyr::recode(Employment_type, "A1" = 1, "A2" = 2, "A3"=3, "A4" = 4,
- "A5" = 5, "A6" = 6),
- Conseq_UGS = dplyr::recode(Conseq_UGS, "A1" = 5, "A2" = 4, "A3"=3, "A4" = 2, "A5" = 1, "A6" = NA_real_),
- Conseq_Money = dplyr::recode(Conseq_Money, "A1" = 5, "A2" = 4, "A3"=3, "A4" = 2, "A5" = 1, "A6" = NA_real_))
-
-database_full <- database_full %>% mutate(Gender_female = case_when(Gender == 2 ~1, TRUE~0),
- Age = 2023-Birthyear,
- Uni_degree = case_when(Education == 5 ~1, TRUE~0),
- Kids_Dummy = case_when(Number_Kids > 0 ~ 1, TRUE ~0),
- Employment_full = case_when(Employment_type == 1 ~ 1, TRUE~0),
- Pensioner = case_when(Employment_type == 6 ~ 1, TRUE~0),
- Age_mean = Age - mean(Age),
- Income_mean = QFIncome - mean(QFIncome))
-
-# Data cleaning
-
-
-
-database_full <- database_full %>% filter(Rent_SQ <= 10000 & Rent_SQ >=50) %>%
- filter(WalkingDistance_SQ > 0 & WalkingDistance_SQ <= 300) %>%
- filter(Gender!=3)
-
-database <- database %>% filter(Rent_SQ <= 10000 & Rent_SQ >=50) %>%
- filter(WalkingDistance_SQ > 0 & WalkingDistance_SQ <= 300)
-
-
-summary(database_full$interviewtime)
-
-database_full <- database_full %>% filter(interviewtime >= 300) # make change time to 10 seconds?
-
-
-database_full <- database_full %>%
- filter(!is.na(Treatment_new)) %>%
- mutate(Treatment_A = case_when(
- Treatment == 1 ~ "Treated",
- Treatment == 2 ~ "Vol_Treated",
- Treatment == 3 ~ "Not_Treated",
- TRUE ~ NA_character_
- )) %>%
- mutate(Treatment_B = case_when(
- Treatment_new == 1 | Treatment_new == 2 | Treatment_new == 4 | Treatment_new == 5 ~ "Treated",
- Treatment_new == 3 | Treatment_new == 6 ~ "Not_Treated"
- )) %>%
- mutate(Treatment_C = case_when(
- Treatment_new == 1 ~ 'Video 1',
- Treatment_new == 2 ~ 'No Video 1',
- Treatment_new == 3 ~ 'No Info 2',
- Treatment_new == 4 ~ 'No Video 2',
- Treatment_new == 5 ~ 'Video 2',
- Treatment_new == 6 ~ 'No Treatment 3',
- TRUE ~ NA_character_
- )) %>%
- mutate(Treatment_D = case_when(
- Treatment_new == 1 | Treatment_new == 2 ~ 'Treated',
- Treatment_new == 3 ~ 'No Info 2',
- Treatment_new == 4 | Treatment_new == 5 ~ 'Vol. Treated',
- Treatment_new == 6 ~ 'No Treatment 3',
- TRUE ~ NA_character_
- ))
-
-id_list <- unique(database_full$id)
-
-# Do we sill want to use this? or only database full?
-database <- database %>% filter(id %in% id_list) %>% filter(!is.na(Treatment_new))
-# Building NR Index
-
-for (i in 1:21) {
- variable_name <- paste0("Q38S", sprintf("%02d", i), "W3") # Generate variable name
- cat("Table for", variable_name, ":\n")
- print(table(database_full[[variable_name]]))
- cat("\n")
- database_full[[variable_name]] <- as.numeric(factor(database_full[[variable_name]], levels = c("A1", "A2", "A3", "A4", "A5")))
- cat("Table for", variable_name, ":\n")
- print(table(database_full[[variable_name]]))
- cat("\n")
-}
-
-variables_to_reverse <- c("Q38S02W3", "Q38S03W3", "Q38S10W3", "Q38S11W3", "Q38S13W3", "Q38S14W3", "Q38S15W3", "Q38S18W3")
-for (variable_name in variables_to_reverse) {
- cat("Table for", variable_name, ":\n")
-
- # Convert the variable to a factor with numerical levels and reverse the scores
- database_full[[variable_name]] <- 6 - as.numeric(database_full[[variable_name]])
-
- # Print the table
- print(table(database_full[[variable_name]]))
- cat("\n")
-}
-q38_variables <- grep("^Q38", names(database_full), value = TRUE)
-database_full$Total_NR <- rowSums(database_full[q38_variables])
-hist(database_full$Total_NR)
-database_full <- database_full %>%
- mutate(Mean_NR=Total_NR/21)
-mean_nr<-mean(database_full$Mean_NR, na.rm = TRUE)
-sd_nr<-sd(database_full$Mean_NR, na.rm = TRUE)
-database_full <- database_full %>%
- mutate(Z_Mean_NR=(Mean_NR-mean_nr)/sd_nr)
-database$Z_Mean_NR<- database_full$Z_Mean_NR
-summary(database$Z_Mean_NR)
-
-#Self-Reference Index
-
-for (i in 8:10) {
- variable_name <- paste0("TV", sprintf("%02d", i), "W3") # Generate variable name
- cat("Table for", variable_name, ":\n")
- print(table(database_full[[variable_name]]))
- cat("\n")
- database_full[[variable_name]] <- as.numeric(factor(database_full[[variable_name]], levels = c("A1", "A2", "A3", "A4", "A5")))
- cat("Table for", variable_name, ":\n")
- print(table(database_full[[variable_name]]))
- cat("\n")
-}
-
-
-database_full$Total_SR <- database_full$TV08W3+database_full$TV09W3+database_full$TV10W3
-hist(database_full$Total_SR)
-database_full <- database_full %>%
- mutate(Mean_SR=Total_SR/3)
-mean_sr<-mean(database_full$Mean_SR, na.rm = TRUE)
-sd_sr<-sd(database_full$Mean_SR, na.rm = TRUE)
-database_full <- database_full %>%
- mutate(Z_Mean_SR=(Mean_SR-mean_sr)/sd_sr)
-database$Z_Mean_SR<- database_full$Z_Mean_SR
-summary(database$Z_Mean_SR)
+# Prepare variables that we want to use
+
+database_full <- database_full %>% rename(Gender = "Q03W123", Education = "Q06W123", HHSize = "Q41W123",
+ WorkingTime = "Q44W123", Birthyear = "Q01W123", Rent_net = "Q07W123",
+ Number_Kids = "Q42W123", Employment_type = "Q43W123", Conseq_UGS = "Q28W3",
+ Conseq_Money = "Q29W3", UGS_visits = "Q23W123")
+
+
+database_full <- database_full %>% mutate(Gender = dplyr::recode(Gender, "A1" = 1, "A2" = 2, "A3"=3),
+ Education = dplyr::recode(Education, "A1" = 1, "A2" = 2, "A3"=3, "A4" = 4, "A5" = 5),
+ Employment_type = dplyr::recode(Employment_type, "A1" = 1, "A2" = 2, "A3"=3, "A4" = 4,
+ "A5" = 5, "A6" = 6),
+ Conseq_UGS = dplyr::recode(Conseq_UGS, "A1" = 5, "A2" = 4, "A3"=3, "A4" = 2, "A5" = 1, "A6" = NA_real_),
+ Conseq_Money = dplyr::recode(Conseq_Money, "A1" = 5, "A2" = 4, "A3"=3, "A4" = 2, "A5" = 1, "A6" = NA_real_),
+ UGS_visits = dplyr::recode(UGS_visits, "A1" = 1, "A2" = 2, "A3"=3, "A4" = 4, "A5" = 5, "A6" = 6,
+ "A7" = 7, "A8" = 8, "A9" = 9, "A10"= 10))
+
+database_full <- database_full %>% mutate(Gender_female = case_when(Gender == 2 ~1, TRUE~0),
+ Age = 2023-Birthyear,
+ Uni_degree = case_when(Education == 5 ~1, TRUE~0),
+ Kids_Dummy = case_when(Number_Kids > 0 ~ 1, TRUE ~0),
+ Employment_full = case_when(Employment_type == 1 ~ 1, TRUE~0),
+ Pensioner = case_when(Employment_type == 6 ~ 1, TRUE~0),
+ Age_mean = Age - mean(Age),
+ Income_mean = QFIncome - mean(QFIncome))
+
+# Data cleaning
+
+
+
+database_full <- database_full %>% filter(Rent_SQ <= 10000 & Rent_SQ >=50) %>%
+ filter(WalkingDistance_SQ > 0 & WalkingDistance_SQ <= 300) %>%
+ filter(Gender!=3)
+
+database <- database %>% filter(Rent_SQ <= 10000 & Rent_SQ >=50) %>%
+ filter(WalkingDistance_SQ > 0 & WalkingDistance_SQ <= 300)
+
+
+summary(database_full$interviewtime)
+
+database_full <- database_full %>% filter(interviewtime >= 300) # make change time to 10 seconds?
+
+
+database_full <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Treatment_A = case_when(
+ Treatment == 1 ~ "Treated",
+ Treatment == 2 ~ "Vol_Treated",
+ Treatment == 3 ~ "Not_Treated",
+ TRUE ~ NA_character_
+ )) %>%
+ mutate(Treatment_B = case_when(
+ Treatment_new == 1 | Treatment_new == 2 | Treatment_new == 4 | Treatment_new == 5 ~ "Treated",
+ Treatment_new == 3 | Treatment_new == 6 ~ "Not_Treated"
+ )) %>%
+ mutate(Treatment_C = case_when(
+ Treatment_new == 1 ~ 'Video 1',
+ Treatment_new == 2 ~ 'No Video 1',
+ Treatment_new == 3 ~ 'No Info 2',
+ Treatment_new == 4 ~ 'No Video 2',
+ Treatment_new == 5 ~ 'Video 2',
+ Treatment_new == 6 ~ 'No Treatment 3',
+ TRUE ~ NA_character_
+ )) %>%
+ mutate(Treatment_D = case_when(
+ Treatment_new == 1 | Treatment_new == 2 ~ 'Treated',
+ Treatment_new == 3 ~ 'No Info 2',
+ Treatment_new == 4 | Treatment_new == 5 ~ 'Vol. Treated',
+ Treatment_new == 6 ~ 'No Treatment 3',
+ TRUE ~ NA_character_
+ ))
+
+id_list <- unique(database_full$id)
+
+# Do we sill want to use this? or only database full?
+database <- database %>% filter(id %in% id_list) %>% filter(!is.na(Treatment_new))
+# Building NR Index
+
+for (i in 1:21) {
+ variable_name <- paste0("Q38S", sprintf("%02d", i), "W3") # Generate variable name
+ cat("Table for", variable_name, ":\n")
+ print(table(database_full[[variable_name]]))
+ cat("\n")
+ database_full[[variable_name]] <- as.numeric(factor(database_full[[variable_name]], levels = c("A1", "A2", "A3", "A4", "A5")))
+ cat("Table for", variable_name, ":\n")
+ print(table(database_full[[variable_name]]))
+ cat("\n")
+}
+
+variables_to_reverse <- c("Q38S02W3", "Q38S03W3", "Q38S10W3", "Q38S11W3", "Q38S13W3", "Q38S14W3", "Q38S15W3", "Q38S18W3")
+for (variable_name in variables_to_reverse) {
+ cat("Table for", variable_name, ":\n")
+
+ # Convert the variable to a factor with numerical levels and reverse the scores
+ database_full[[variable_name]] <- 6 - as.numeric(database_full[[variable_name]])
+
+ # Print the table
+ print(table(database_full[[variable_name]]))
+ cat("\n")
+}
+q38_variables <- grep("^Q38", names(database_full), value = TRUE)
+database_full$Total_NR <- rowSums(database_full[q38_variables])
+hist(database_full$Total_NR)
+database_full <- database_full %>%
+ mutate(Mean_NR=Total_NR/21)
+mean_nr<-mean(database_full$Mean_NR, na.rm = TRUE)
+sd_nr<-sd(database_full$Mean_NR, na.rm = TRUE)
+database_full <- database_full %>%
+ mutate(Z_Mean_NR=(Mean_NR-mean_nr)/sd_nr)
+database$Z_Mean_NR<- database_full$Z_Mean_NR
+summary(database$Z_Mean_NR)
+
+#Self-Reference Index
+
+for (i in 8:10) {
+ variable_name <- paste0("TV", sprintf("%02d", i), "W3") # Generate variable name
+ cat("Table for", variable_name, ":\n")
+ print(table(database_full[[variable_name]]))
+ cat("\n")
+ database_full[[variable_name]] <- as.numeric(factor(database_full[[variable_name]], levels = c("A1", "A2", "A3", "A4", "A5")))
+ cat("Table for", variable_name, ":\n")
+ print(table(database_full[[variable_name]]))
+ cat("\n")
+}
+
+
+database_full$Total_SR <- database_full$TV08W3+database_full$TV09W3+database_full$TV10W3
+hist(database_full$Total_SR)
+database_full <- database_full %>%
+ mutate(Mean_SR=Total_SR/3)
+mean_sr<-mean(database_full$Mean_SR, na.rm = TRUE)
+sd_sr<-sd(database_full$Mean_SR, na.rm = TRUE)
+database_full <- database_full %>%
+ mutate(Z_Mean_SR=(Mean_SR-mean_sr)/sd_sr)
+database$Z_Mean_SR<- database_full$Z_Mean_SR
+summary(database$Z_Mean_SR)
diff --git a/Scripts/logit/chr_vol_treat.R b/Scripts/logit/chr_vol_treat.R
index 133e96ccdf89ce592f098fde845161ffeafc199e..43951bffcc20725990c49d085d91d3e3d660942c 100644
--- a/Scripts/logit/chr_vol_treat.R
+++ b/Scripts/logit/chr_vol_treat.R
@@ -1,4 +1,5 @@
library(margins)
+library(pROC)
# Test treatment effect
@@ -8,7 +9,9 @@ database_full <- database_full %>%
Dummy_Video_2 = case_when(Treatment_new == 5 ~ 1, TRUE ~ 0),
Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
Dummy_Info_nv1 = case_when(Treatment_new == 2 ~1, TRUE~0),
- Dummy_Info_nv2 = case_when(Treatment_new == 4 ~1 , TRUE~0))
+ Dummy_Info_nv2 = case_when(Treatment_new == 4 ~1 , TRUE~0)) %>%
+ mutate(Engagement_ugs = groupTime1731 + groupTime1726 + groupTime1769 + groupTime1727 + groupTime1770 + groupTime1771 + groupTime1768 +
+ groupTime1738)
@@ -33,9 +36,12 @@ summary(logit_choice_treat)
logit_choice_treat_uni<-glm(Choice_Treat ~ as.factor(Gender)+Z_Mean_NR+Age_mean + QFIncome +
- Uni_degree + Kids_Dummy+ as.factor(Q27W123), data, family=binomial)
+ Uni_degree + Kids_Dummy + Engagement_ugs + UGS_visits, data, family=binomial)
summary(logit_choice_treat_uni)
+
+logit_sig <- glm(Choice_Treat ~ Z_Mean_NR + Age_mean + QFIncome + UGS_visits, data, family=binomial)
+
# Calculate marginal effects
marginal_effects <- margins(logit_choice_treat_uni)
@@ -43,14 +49,45 @@ marginal_effects <- margins(logit_choice_treat_uni)
summary(marginal_effects)
+cut_off <- 0.45
# Make predictions
-data$probabilities <- logit_choice_treat_uni %>% predict(data, type = "response")
-data$predicted.classes <- ifelse(data$probabilities >= 0.5, 1, 0)
+data$probabilities <- logit_sig %>% predict(data, type = "response")
+data$predicted.classes <- ifelse(data$probabilities >= cut_off, 1, 0)
# Model accuracy
mean(data$predicted.classes == data$Choice_Treat, na.rm = T)
-table(predicted.classes ,data$Choice_Treat)
-
+table(data$predicted.classes ,data$Choice_Treat)
+
+calculate_metrics <- function(confusion_matrix) {
+ # Extract values from the confusion matrix
+ TN <- confusion_matrix[1, 1]
+ FP <- confusion_matrix[2, 1]
+ FN <- confusion_matrix[1, 2]
+ TP <- confusion_matrix[2, 2]
+
+ # Calculate sensitivity
+ sensitivity <- round(TP / (TP + FN), 2)
+
+ # Calculate specificity
+ specificity <- round(TN / (TN + FP), 2)
+
+ # Return the results as a list
+ return(list(sensitivity = sensitivity, specificity = specificity))
+}
+
+# Calculate metrics
+metrics <- calculate_metrics(table(data$predicted.classes ,data$Choice_Treat))
+
+# Print results
+print(paste("Sensitivity:", metrics$sensitivity))
+print(paste("Specificity:", metrics$specificity))
+roc_obj <- roc(data$Choice_Treat, data$probabilities)
+plot(roc_obj, main = "ROC Curve", col = "blue", lwd = 2)
+auc_value <- auc(roc_obj)
+best_coords <- coords(roc_obj, "best", best.method="youden")
+
+
+cut_off <- best_coords$threshold
diff --git a/Scripts/mxl/mxl_wtp_space_caseE.R b/Scripts/mxl/mxl_wtp_space_caseE.R
new file mode 100644
index 0000000000000000000000000000000000000000..d7b40621d7bc35b4bbd35c5ecf815104de135ca7
--- /dev/null
+++ b/Scripts/mxl/mxl_wtp_space_caseE.R
@@ -0,0 +1,175 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database_full$probabilities <- logit_sig %>% predict(database_full, type = "response")
+database_full$predicted.classes <- ifelse(database_full$probabilities >= cut_off, 1, 0)
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
+ Dummy_pred_Treated = case_when(Treatment_new == 6 & predicted.classes == 1 ~ 1, TRUE~0))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_E base",
+ modelDescr = "MXL wtp space Case E base",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_sq_pred_treat = 0,
+ mu_nat_treated =0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_nat_pred_treat = 0,
+ mu_walking_treated =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ mu_walking_pred_treat = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 2)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+### Set parameters for generating draws, use 2000 sobol draws
+apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = n_draws,
+ interUnifDraws = c(),
+ interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters, define distribution of the parameters
+apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
+ randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
+ randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
+ randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+
+ return(randcoeff)
+}
+
+
+### validate
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ #### List of utilities (later integrated in mnl_settings below) ####
+ # Define utility functions here:
+
+ V = list()
+ V[['alt1']] = -(b_mu_rent)*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated - Rent_1
+ + mu_nat_pred_treat * Naturalness_1 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_1 *Dummy_pred_Treated)
+
+ V[['alt2']] = -(b_mu_rent)*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated- Rent_2
+ + mu_nat_pred_treat * Naturalness_2 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_2 *Dummy_pred_Treated)
+
+ V[['alt3']] = -(b_mu_rent)*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info - Rent_3
+ + mu_nat_pred_treat * Naturalness_3 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_3 *Dummy_pred_Treated
+ + mu_ASC_sq_pred_treat * Dummy_pred_Treated)
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V#, # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ##
+# ################################################################# #
+# estimate model with bfgs algorithm
+
+mxl_wtp_case_e = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(mxl_wtp_case_e)
+
+
diff --git a/Scripts/mxl/mxl_wtp_space_caseF.R b/Scripts/mxl/mxl_wtp_space_caseF.R
new file mode 100644
index 0000000000000000000000000000000000000000..1bc9c4e08f19e756f7091baa5b973ac747c6a2fe
--- /dev/null
+++ b/Scripts/mxl/mxl_wtp_space_caseF.R
@@ -0,0 +1,183 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database_full$probabilities <- logit_sig %>% predict(database_full, type = "response")
+database_full$predicted.classes <- ifelse(database_full$probabilities >= cut_off, 1, 0)
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
+ Dummy_pred_Treated = case_when(Treatment_new == 6 & predicted.classes == 1 ~ 1, TRUE~0),
+ Dummy_Treated_predicted = case_when(Treatment_new == 1 & predicted.classes == 1|Treatment_new == 2 & predicted.classes == 1 ~ 1, TRUE ~ 0),
+ Dummy_Treated_notpredicted = case_when(Treatment_new == 1 & predicted.classes == 0|Treatment_new == 2 & predicted.classes == 0 ~ 1, TRUE ~ 0))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_F base",
+ modelDescr = "MXL wtp space Case F base",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated_predicted = 0,
+ mu_ASC_sq_treated_notpredicted = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_sq_pred_treat = 0,
+ mu_nat_treated_predicted =0,
+ mu_nat_treated_notpredicted = 0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_nat_pred_treat = 0,
+ mu_walking_treated_predicted =0,
+ mu_walking_treated_notpredicted =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ mu_walking_pred_treat = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 2)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+### Set parameters for generating draws, use 2000 sobol draws
+apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = n_draws,
+ interUnifDraws = c(),
+ interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters, define distribution of the parameters
+apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
+ randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
+ randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
+ randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+
+ return(randcoeff)
+}
+
+
+### validate
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ #### List of utilities (later integrated in mnl_settings below) ####
+ # Define utility functions here:
+
+ V = list()
+ V[['alt1']] = -(b_mu_rent)*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated_predicted * Naturalness_1 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_1 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_1 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_1 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated - Rent_1
+ + mu_nat_pred_treat * Naturalness_1 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_1 * Dummy_pred_Treated)
+
+ V[['alt2']] = -(b_mu_rent)*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated_predicted * Naturalness_2 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_2 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_2 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_2 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated- Rent_2
+ + mu_nat_pred_treat * Naturalness_2 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_2 * Dummy_pred_Treated)
+
+ V[['alt3']] = -(b_mu_rent)*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated_predicted * Naturalness_3 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_3 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_3 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_3 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated_predicted * Dummy_Treated_predicted + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info - Rent_3 + mu_ASC_sq_treated_notpredicted * Dummy_Treated_notpredicted
+ + mu_nat_pred_treat * Naturalness_3 * Dummy_pred_Treated + mu_walking_pred_treat * WalkingDistance_3 * Dummy_pred_Treated
+ + mu_ASC_sq_pred_treat * Dummy_pred_Treated)
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V#, # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ##
+# ################################################################# #
+# estimate model with bfgs algorithm
+
+mxl_wtp_case_f = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(mxl_wtp_case_f)
+
+
diff --git a/Scripts/mxl/mxl_wtp_space_caseG.R b/Scripts/mxl/mxl_wtp_space_caseG.R
new file mode 100644
index 0000000000000000000000000000000000000000..1fec42ef9d90019640ef53c7ade90a0c1b55b33c
--- /dev/null
+++ b/Scripts/mxl/mxl_wtp_space_caseG.R
@@ -0,0 +1,176 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database_full$probabilities <- logit_sig %>% predict(database_full, type = "response")
+database_full$predicted.classes <- ifelse(database_full$probabilities >= cut_off, 1, 0)
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
+ Dummy_pred_Treated = case_when(Treatment_new == 6 & predicted.classes == 1 ~ 1, TRUE~0),
+ Dummy_Treated_predicted = case_when(Treatment_new == 1 & predicted.classes == 1|Treatment_new == 2 & predicted.classes == 1 ~ 1, TRUE ~ 0),
+ Dummy_Treated_notpredicted = case_when(Treatment_new == 1 & predicted.classes == 0|Treatment_new == 2 & predicted.classes == 0 ~ 1, TRUE ~ 0))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_G base",
+ modelDescr = "MXL wtp space Case G base",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated_predicted = 0,
+ mu_ASC_sq_treated_notpredicted = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_nat_treated_predicted =0,
+ mu_nat_treated_notpredicted = 0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_walking_treated_predicted =0,
+ mu_walking_treated_notpredicted =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 2)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+### Set parameters for generating draws, use 2000 sobol draws
+apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = n_draws,
+ interUnifDraws = c(),
+ interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters, define distribution of the parameters
+apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
+ randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
+ randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
+ randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+
+ return(randcoeff)
+}
+
+
+### validate
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ #### List of utilities (later integrated in mnl_settings below) ####
+ # Define utility functions here:
+
+ V = list()
+ V[['alt1']] = -(b_mu_rent)*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated_predicted * Naturalness_1 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_1 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_1 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_1 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated - Rent_1)
+
+ V[['alt2']] = -(b_mu_rent)*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated_predicted * Naturalness_2 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_2 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_2 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_2 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated- Rent_2)
+
+ V[['alt3']] = -(b_mu_rent)*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated_predicted * Naturalness_3 *Dummy_Treated_predicted + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated + mu_nat_treated_notpredicted * Naturalness_3 *Dummy_Treated_notpredicted
+ + mu_walking_treated_predicted * WalkingDistance_3 *Dummy_Treated_predicted + mu_walking_treated_notpredicted * WalkingDistance_3 *Dummy_Treated_notpredicted
+ + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated_predicted * Dummy_Treated_predicted + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info - Rent_3 + mu_ASC_sq_treated_notpredicted * Dummy_Treated_notpredicted)
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V#, # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ##
+# ################################################################# #
+# estimate model with bfgs algorithm
+
+mxl_wtp_case_g = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(mxl_wtp_case_g)
+
+
diff --git a/custom_app.lua b/custom_app.lua
new file mode 100644
index 0000000000000000000000000000000000000000..b9f5efff21699140054871a4870f78bb7f6fa60c
--- /dev/null
+++ b/custom_app.lua
@@ -0,0 +1,16 @@
+local in_appendix = false
+
+Header = function(h)
+ if h.level == 1 then
+ if h.classes:includes("appendix") then
+ in_appendix = true
+ h.attributes["visibility"] = "uncounted"
+ else
+ in_appendix = false
+ end
+ end
+ if h.level == 2 and in_appendix then
+ h.attributes["visibility"] = "uncounted"
+ end
+ return h
+end
diff --git a/project_start.qmd b/project_start.qmd
index aebd3fa49f4b97931a54f8dbd2cebbb0758958a1..eb41fc3768f60987b4a048d73eee5d7de771eeaf 100644
--- a/project_start.qmd
+++ b/project_start.qmd
@@ -20,7 +20,7 @@ format:
slide-number: true
smaller: true
logo: Grafics/iDiv_logo_item.PNG
- footer: "WONV 2024: Hot Cities, Cool Choices"
+ footer: "DCE Network meeting"
scrollable: true
embed-resources: true
---
@@ -155,7 +155,7 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
+ **Socio-demographics**: Age, Gender, Income, Education
-+ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness** (NR-Index) [@nisbet2009nature]
++ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness** [@nisbet2009nature]
:::
@@ -237,11 +237,11 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
<!-- ::: -->
-## OLS: Engagement
-::: panel-tabset
-### OLS Specification
+
+
+## OLS Specification
```{=tex}
\begin{equation}
@@ -277,13 +277,12 @@ with
```
-### Results
+## OLS: Engagement
```{r}
ggpubr::ggarrange(plot_interview_A, plot_cc_A)
```
-:::
## OLS: Information Recall & Consequentiality
@@ -293,13 +292,11 @@ ggpubr::ggarrange(plot_mani_A, plot_cons_A)
## MXL: Effects on Stated Preferences
-::: panel-tabset
-### MXL Specification
-```{=tex}
+```{=latex}
\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
+ U_i = -\beta_{C_i} \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}
```
@@ -322,20 +319,20 @@ with
\end{equation}
```
-### Results
+## MXL: Effects on Stated Preferences
-::: {style="font-size: 90%;"}
+::: {style="font-size: 80%;"}
```{r, results='asis'}
htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])), single.row = TRUE,
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "ASC_sq" = "ASC SQ", "rent" = "Rent",
+ "_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.")
```
-:::
+
:::
@@ -398,8 +395,8 @@ ggpubr::ggarrange(plot_mani_D, plot_cons_D)
::: {style="font-size: 80%;"}
```{r, results='asis'}
htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "ASC_sq" = "ASC SQ", "rent" = "Rent",
+ "_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", "Treated", "Vol. Treated", "No Info"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
@@ -414,8 +411,8 @@ htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
::: {style="font-size: 80%;"}
```{r, results='asis'}
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",
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "ASC_sq" = "ASC SQ", "rent" = "Rent",
+ "_natural" = "Naturalness", "nat" = "Naturalness",
"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", "Treated", "Vol. Treated", "No Info", "NR-Index"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
diff --git a/project_start_wonv.qmd b/project_start_wonv.qmd
new file mode 100644
index 0000000000000000000000000000000000000000..aebd3fa49f4b97931a54f8dbd2cebbb0758958a1
--- /dev/null
+++ b/project_start_wonv.qmd
@@ -0,0 +1,598 @@
+---
+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
+ data-background-opacity: "0.2"
+author: "Nino Cavallaro, Fabian Marder, Julian Sagebiel"
+institute:
+- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
+- Leipzig University
+date: today
+date-format: long
+bibliography: references.bib
+filters:
+ - parse-latex
+ - custom_app.lua
+format:
+ revealjs:
+ slide-number: true
+ smaller: true
+ logo: Grafics/iDiv_logo_item.PNG
+ footer: "WONV 2024: 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" = "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", "as.factor(Treatment_D)Treated" = "Treated", "as.factor(Treatment_D)Vol. Treated" = "Vol. Treated",
+ "as.factor(Treatment_D)No Info 2" = "No Info",
+ "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
+- **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 provision** 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
+- **Optional information** allows the respondents to gather required information if needed and might increase efficiency of information provision
+
+- Providing optional information should enhance optimal information seeking leading to less heterogeneity in good-specific knowledge between the respondents
+:::
+
+## Literature
+
+::: incremental
+- There is **little research** on the effects of **optional information provision** on choice behavior and information recall
+- In their study, @tienhaara2022information surveyed preferences for agricultural genetic resources, allowing respondents the option to access detailed information on the valued goods prior to preference elicitation
+- Similarly, @hu2009consumers offered respondents the opportunity to access optional information about genetic modified food before participating in a choice experiment
+- Both studies conclude that, on average, respondents who voluntary retrieve information have **larger willingness to pay** for the good to be valued
+- Their study design, however, does not allow comparing the optional information retrieval to a version where the additional information was shown obligatory
+:::
+
+## Research Contribution
+
+::: incremental
+- 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 @welling2023information, expanding our understanding of treatment effects
+- We test who chooses additional information and to what extent their preferences differ from those who do not choose additional information
+:::
+
+## Research Questions
+
+::: incremental
+1. Do obligatory and optional information provision affect **survey engagement**, **information recall**, **consequentiality**, and **stated preferences**?
+
+- OLS and MXL with interactions
+
+2. Do **socio-demographic** or **attitudinal** variables influence the decision to **access optional information**?
+
+- Logit regression
+
+3. Do **survey engagement**, **information recall**, **consequentiality**, and **stated preferences** differ between respondents who **voluntary access information** and those who do not?
+
+- OLS and MXL with interactions
+:::
+
+# Survey & Data
+
+## Discrete Choice Experiment
+
+::: incremental
+- To investigate the research questions, we use data from a **discrete choice experiment (DCE)** on naturalness of urban green spaces in the 14 largest German cities
+- The survey is an exact **replication** of the choice experiment of @Bronnmann062321-0072R1 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)
+
+- Info text about the effect of **natural urban green spaces** on urban **heat islands**
+- **Optional video** (2 minutes) with the almost the same information
+
+{width="200"}
+
+## Treatment (Quiz & Self Reference Questions)
+
+::: incremental
+- **Seven quiz questions** with strict reference to the previously provided information
+
+- Example: *The temperature difference between the city and the surrounding area can be up to 10 degrees Celsius. (true/false)*
+
+- **Two self reference questions**
+
+- Example: *The city should do more to avoid heat islands. (Strongly agree - Strongly disagree)*
+:::
+
+
+
+
+
+
+
+## Additional 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**
+
++ Two questions on **consequentiality **
+
+- Example: *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)*
+
++ **Socio-demographics**: Age, Gender, Income, Education
+
++ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness** (NR-Index) [@nisbet2009nature]
+
+:::
+
+
+## Experimental Setting
+
+{width="300"}
+
+
+<!-- ## Methods (1) {auto-animate="true"} -->
+
+
+<!-- - OLS regression (survey engagement, information recall, consequentiality): -->
+
+<!-- ```{=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: -->
+
+
+<!-- ## 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, Optional Treatment\} -->
+<!-- \end{equation} -->
+<!-- ``` -->
+<!-- ```{=tex} -->
+<!-- \begin{equation} -->
+<!-- v_{Treat_B} = \{Treated, Vol. Treated, No Info\} -->
+<!-- \end{equation} -->
+<!-- ``` -->
+
+# Case A: Obligatory vs. Optional Information
+
+
+## Case A
+
+1. Do obligatory and optional information provision affect **survey engagement**, **information recall**, **consequentiality**, and **stated preferences**?
+
+{width="300"}
+
+<!-- ::: {style="font-size: 45%;"} -->
+
+<!-- ```{r, results='asis'} -->
+
+<!-- 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.") -->
+
+<!-- ``` -->
+
+<!-- ::: -->
+
+## OLS: Engagement
+
+::: panel-tabset
+
+### OLS Specification
+
+```{=tex}
+\begin{equation}
+ Y = \beta_0 + \beta_{Treat} \cdot v_{Treat} + \beta_{SocDem} \cdot v_{SocDem} + \epsilon
+ \label{olss}
+\end{equation}
+```
+
+with
+
+```{=tex}
+\begin{equation}
+ Y = \left\{
+ \begin{aligned}
+ &\text{Net Interview Time, Mean Choice Card Time} \\
+ &\text{Percentage of correct quiz statements, Consequentiality Score}
+ \end{aligned}
+ \right\}
+\end{equation}
+```
+
+
+```{=tex}
+\begin{equation}
+ v_{Treat} = \{\text{Treated, Optional Treatment}\}
+\end{equation}
+```
+
+```{=tex}
+\begin{equation}
+ v_{SocDem} = \{\text{Age, Gender, Income, Education, NR-Index}\}
+\end{equation}
+```
+
+
+### Results
+
+```{r}
+ggpubr::ggarrange(plot_interview_A, plot_cc_A)
+```
+
+:::
+
+## OLS: Information Recall & Consequentiality
+
+```{r}
+ggpubr::ggarrange(plot_mani_A, plot_cons_A)
+```
+
+## MXL: Effects on Stated Preferences
+
+::: panel-tabset
+
+### MXL Specification
+
+```{=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}
+```
+with
+
+```{=tex}
+\begin{equation}
+ v_{X_i} = \{ASC_{sq_i}, Nat_i, WD_i\}
+\end{equation}
+```
+
+```{=tex}
+\begin{equation}
+ v_{Treat} = \{\text{Treated, Optional Treatment}\}
+\end{equation}
+```
+```{=tex}
+\begin{equation}
+ C_i = \{Rent_i\}
+\end{equation}
+```
+
+### Results
+
+::: {style="font-size: 90%;"}
+```{r, results='asis'}
+htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])), single.row = TRUE,
+ 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.")
+```
+:::
+
+
+:::
+
+# Case B: Voluntary Information Access
+
+## Voluntary Information Access
+
+2. Do **socio-demographic** or **attitudinal** variables influence the decision to **access optional information**?
+
+{width="300"}
+
+
+
+## Logit Regression: Who chooses Optional Information?
+
+```{=tex}
+\begin{equation}
+ Y = \beta_0 + \beta_{SocDem} \cdot v_{SocDem} + \epsilon
+ \label{simple_logit}
+\end{equation}
+```
+
+::: {style="font-size: 58%;"}
+```{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 B
+
+3. Do **survey engagement**, **information recall**, **consequentiality**, and **stated preferences** differ between respondents who **voluntary access information** and those who do not?
+
+{width="300"}
+
+## OLS Engagement: Interview & Choice Card Time
+
+```{r}
+ggpubr::ggarrange(plot_interview_D, plot_cc_D)
+```
+
+## OLS: Information Recall & Consequentiality
+
+```{r}
+ggpubr::ggarrange(plot_mani_D, plot_cons_D)
+```
+
+## MXL: Case B
+
+::: {style="font-size: 80%;"}
+```{r, results='asis'}
+htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
+ 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", "Treated", "Vol. Treated", "No Info"), custom.note = "%stars (one-sided). Robust 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: Case B with NR-index Interaction
+
+::: {style="font-size: 80%;"}
+```{r, results='asis'}
+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", "ASC" ="ASC SQ",
+ "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+ 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_C_NR",
+ caption = "Results of mixed logit model with treatment interactions for Case B including NR-index.")
+```
+:::
+
+# Discussion
+
+## Discussion (1)
+
+1. Do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences?
+
+::: incremental
+- Obligatory and optional treatments do not increase survey engagement measured via time spend on the survey
+
+- Both treatments increase information recall, stronger effect for obligatory treatment
+
+- No effects on consequentiality
+
+- Strong effects on stated preferences for both treatments, more pronounced effect for the obligatory treatment
+:::
+
+## Discussion (2)
+
+2. Do socio-demographic or attitudinal variables influence the decision to access optional 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 (NR-index) influence the likelihood of accessing additional information
+:::
+
+## Discussion (3)
+
+3. Do survey engagement, information recall, consequentiality, and stated preferences differ between respondents who voluntary access information and those who do not?
+
+::: incremental
+- Respondents that voluntary access information do not engage more in the survey, but perform best in the quiz
+
+- Respondents that decide to not access additional information engage less in the survey, have a lower consequentiality score and do not perform different in the quiz than the non-treated respondents
+
+- Highest willingness to pay values in the group that voluntary accesses information
+:::
+
+## Conclusion
+
+::: incremental
+- Providing optional information does not lead to optional information seeking
+
+- Optional information is mostly accessed by people that are interested in the good to be valued
+
+- We recommend to use obligatory information provision rather than optional one
+:::
+
+## References
+
+::: {#refs}
+:::
+
+# Appendix {.appendix}
+
+
+Information provision (Video) Link to the video: <https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4>
+
+## Summary Statistics
+
+::: {style="font-size: 55%;"}
+```{=latex}
+ \begin{table}[htbp]
+ \centering
+ \begin{tabular}{lcrcclc} \hline
+ Characteristic& Sample & Treated & Opt. Treatment & Non-Treated & Vol. Treated & No Info \\ \hline
+ Gender & &&& & \\
+ \hspace{0.3cm} Proportion Females&50.67\%&50.15\%&47.98\%&49.76\% &50.48\% & 48.26\% \\ \hline
+ Age &44.60&44.91 &44.24&44.63 & 42.51 & 44.97 \\ \hline
+ Net Household Income&&&& & & \\
+ \hspace{0.3cm} Less than 1500\euro &19.06\%&20.43\%&18.86\%&17.85\%& 22.12\% & 19.77\% \\
+ \hspace{0.3cm} 1500\euro -3000\euro &23.39\%&20.59\%&24.75\%&24.96\%& 15.87\% & 26.16\% \\
+ \hspace{0.3cm} 3000\euro -4000\euro &32.62\%&35.14\%&29.63\%&32.86\%& 34.13\% & 27.61\% \\
+ \hspace{0.3cm} More than 4000\euro &19.65\%&19.04\%&20.70\%&19.27\%& 24.52\% & 18.31\% \\ \hline
+ Education & &&&& & \\
+ \hspace{0.3cm} University degree &34.54\%&32.20\%&34.68\%&36.81\%& 33.17\% & 33.72\% \\
+ \hspace{0.3cm} No University degree &64.46\%&67.80\%&65.32\%&63.19\%& 66.83\% &66.28\% \\ \hline
+ NR-index &0.00&0.00&-0.02&0.02& 0.15 &-0.19 \\ \hline
+ Correct quiz answers &66.97\%&69.86\%&66.91\%&64.07\%&69.44\%&63.08\% \\ \hline
+ Consequentiality score &6.15&6.28&6.02&6.12&6.75 &5.76 \\ \hline
+ Net interview time &1396&1340&1424&1426& 1441 &1350 \\
+ Mean Choice Card time &18.86&19.02&19.10&18.51 &19.03&19.24 \\ \hline
+ Number of respondents &1873&646&594&633& 250 &344 \\ \hline
+ \multicolumn{7}{p{\textwidth-2\tabcolsep}}{\scriptsize Notes: Age is measured in years; NR-index is z-standardized with mean=0 and standard deviation=1; Correct quiz answers measure the percentage of correctly answered quiz questions; Consequentiality score is the sum of the two Likert-type questions on consequentiality; Net interview time and Mean Choice card time are measured in seconds; Net interview time is measured as overall interview time minus treatment time; To avoid bias, we excluded the fastest 1\% and the slowest 1\% of net interview time and mean Choice Card time from the analysis.}
+ \end{tabular}
+ \caption{Summary statistics of the sample and by scenarios.}
+ \label{tab:summary}
+ \end{table}
+```
+:::
+
+
+## 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.")
+```
+:::
+
+## OLS Models: Case A
+
+
+::: {style="font-size: 65%;"}
+```{r, results='asis'}
+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",
+ custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
+ Model 1A refers to net interview time,
+ Model 2A denotes choice card time, Model 3A represents the percentage of correct quiz questions, and Model 4A represents consequentiality score.
+ (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
+ in the obligatory treated group, Optional Treatment is a dummy variable indicating membership in the group with
+ the choice to receive treatment or not, with the reference group being Non-Treated. NR-Index represents the z-standardized
+ natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
+ Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
+ a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
+ label = "tab:olsA", single.row = TRUE,
+ caption = "Results of OLS regressions for Scenario Case A.")
+```
+:::
+
+## OLS Models: Case B
+
+::: {style="font-size: 60%;"}
+```{r, results='asis'}
+htmlreg(l=list(ols_time_spent_control_D, ols_time_cc_control_D, ols_percentage_correct_control_D, conseq_model_control_D),
+ custom.model.names = c("Interview Time", "CC Time", "Quiz", "Cons. Score"),
+ custom.header = list("Model 1B" = 1:1, "Model 2B" = 2:2, "Model 3B" = 3:3, "Model 4B" = 4:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
+ Model 1B refers to net interview time,
+ Model 2B denotes choice card time, Model 1B represents the percentage of correct quiz questions, and Model 4B represents consequentiality score.
+ (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
+ in the obligatory treated group, Vol. Treated is a dummy variable indicating the group that voluntarily chose the optional treatment,
+ while No Info indicates the group that did not opt for the treatment, with the reference group being Non-Treated. NR-Index represents the z-standardized
+ natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
+ Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
+ a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
+ label = "tab:olsD",
+ caption = "Results of OLS regressions for Scenario Case B.", single.row = TRUE)
+```
+:::
+
+## 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))
+```
diff --git a/references.bib b/references.bib
new file mode 100644
index 0000000000000000000000000000000000000000..8e4b2f6abbdc86defb1e28f004c39bfb48623752
--- /dev/null
+++ b/references.bib
@@ -0,0 +1,1981 @@
+@article{hu2009consumers,
+ title={Consumers' preferences for GM food and voluntary information access: A simultaneous choice analysis},
+ author={Hu, Wuyang and Adamowicz, Wiktor L and Veeman, Michele M},
+ journal={Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie},
+ volume={57},
+ number={2},
+ pages={241--267},
+ year={2009},
+ publisher={Wiley Online Library}
+}
+
+@article {Bronnmann062321-0072R1,
+ author = {Bronnmann, Julia and Liebelt, Veronika and Marder, Fabian and Meya, Jasper and Quaas, Martin},
+ title = {The Value of Naturalness of Urban Green Spaces: Evidence from a Discrete Choice Experiment},
+ elocation-id = {062321-0072R1},
+ year = {2023},
+ publisher = {University of Wisconsin Press},
+ abstract = {The range of benefits for humans and biodiversity conservation provided by urban green spaces (UGS) receives substantial attention in relation to urban planning and management. However, little is known about the value of nature in UGS. We developed a graphical measurement scale for the naturalness of UGS, with 5 steps between largely sealed and largely wilderness, which was embedded in an online survey and a discrete choice experiment. Using mixed logit models, we find that German citizens have a mean willingness to pay of {\texteuro}20.25 per month for an increase in the naturalness of the closest UGS by one step.},
+ issn = {0023-7639},
+ eprint = {https://le.uwpress.org/content/early/2023/02/17/le.99.4.062321-0072R1.full.pdf},
+ journal = {Land Economics}
+}
+
+@article{tienhaara2022information,
+ title={Information use and its effects on the valuation of agricultural genetic resources},
+ author={Tienhaara, Annika and Ahtiainen, Heini and Pouta, Eija and Czajkowski, Miko{\l}aj},
+ journal={Land Economics},
+ volume={98},
+ number={2},
+ pages={337--354},
+ year={2022},
+ publisher={University of Wisconsin Press}
+}
+
+@article{welling2023information,
+ title={Information processing in stated preference surveys: A case study on urban gardens},
+ author={Welling, Malte and Sagebiel, Julian and Rommel, Jens},
+ journal={Journal of Environmental Economics and Management},
+ volume={119},
+ pages={102798},
+ year={2023},
+ publisher={Elsevier}
+}
+
+% Master thesis papers %
+
+@article{aronson2017biodiversity,
+ title={Biodiversity in the city: key challenges for urban green space management},
+ author={Aronson, Myla FJ and Lepczyk, Christopher A and Evans, Karl L and Goddard, Mark A and Lerman, Susannah B and MacIvor, J Scott and Nilon, Charles H and Vargo, Timothy},
+ journal={Frontiers in Ecology and the Environment},
+ volume={15},
+ number={4},
+ pages={189--196},
+ year={2017},
+ publisher={Wiley Online Library}
+}
+
+
+
+@article{bertram2015preferences,
+ title={Preferences for cultural urban ecosystem services: Comparing attitudes, perception, and use},
+ author={Bertram, Christine and Rehdanz, Katrin},
+ journal={Ecosystem Services},
+ volume={12},
+ pages={187--199},
+ year={2015},
+ publisher={Elsevier}
+}
+
+@article{pulighe2016insights,
+ title={Insights and opportunities from mapping ecosystem services of urban green spaces and potentials in planning},
+ author={Pulighe, Giuseppe and Fava, Francesco and Lupia, Flavio},
+ journal={Ecosystem services},
+ volume={22},
+ pages={1--10},
+ year={2016},
+ publisher={Elsevier}
+}
+
+@article{niemela2010using,
+ title={Using the ecosystem services approach for better planning and conservation of urban green spaces: a Finland case study},
+ author={Niemel{\"a}, Jari and Saarela, Sanna-Riikka and S{\"o}derman, Tarja and Kopperoinen, Leena and Yli-Pelkonen, Vesa and V{\"a}re, Seija and Kotze, D Johan},
+ journal={Biodiversity and Conservation},
+ volume={19},
+ pages={3225--3243},
+ year={2010},
+ publisher={Springer}
+}
+
+@article{ripley1977modelling,
+ title={Modelling spatial patterns},
+ author={Ripley, Brian D},
+ journal={Journal of the Royal Statistical Society: Series B (Methodological)},
+ volume={39},
+ number={2},
+ pages={172--192},
+ year={1977},
+ publisher={Wiley Online Library}
+}
+
+@article{tiebout1956pure,
+ title={A pure theory of local expenditures},
+ author={Tiebout, Charles M},
+ journal={Journal of political economy},
+ volume={64},
+ number={5},
+ pages={416--424},
+ year={1956},
+ publisher={The University Press of Chicago}
+}
+
+@article{lancaster1966new,
+ title={A new approach to consumer theory},
+ author={Lancaster, Kelvin J},
+ journal={Journal of political economy},
+ volume={74},
+ number={2},
+ pages={132--157},
+ year={1966},
+ publisher={The University of Chicago Press}
+}
+
+@article{johnston2021guidance,
+ title={Guidance to enhance the validity and credibility of environmental benefit transfers},
+ author={Johnston, Robert J and Boyle, Kevin J and Loureiro, Maria L and Navrud, St{\aa}le and Rolfe, John},
+ journal={Environmental and Resource Economics},
+ volume={79},
+ number={3},
+ pages={575--624},
+ year={2021},
+ publisher={Springer}
+}
+
+@article{diluiso2021changes,
+ title={Changes in urban green spaces’ value perception: A meta-analytic benefit transfer function for European cities},
+ author={Diluiso, Francesca and Guastella, Gianni and Pareglio, Stefano},
+ journal={Land Use Policy},
+ volume={101},
+ pages={105--116},
+ year={2021},
+ publisher={Elsevier}
+}
+
+@article{brander2011value,
+ title={The value of urban open space: Meta-analyses of contingent valuation and hedonic pricing results},
+ author={Brander, Luke M and Koetse, Mark J},
+ journal={Journal of environmental management},
+ volume={92},
+ number={10},
+ pages={2763--2773},
+ year={2011},
+ publisher={Elsevier}
+}
+
+@article{roberts2022urban,
+ title={Urban residents value multi-functional urban greenspaces},
+ author={Roberts, Michaela and Glenk, Klaus and McVittie, Alistair},
+ journal={Urban Forestry \& Urban Greening},
+ volume={74},
+ pages={127681},
+ year={2022},
+ publisher={Elsevier}
+}
+
+
+@article{lampinen2021acceptance,
+ title={Acceptance of near-natural greenspace management relates to ecological and socio-cultural assigned values among European urbanites},
+ author={Lampinen, Jussi and Tuomi, Maria and Fischer, Leonie K and Neuenkamp, Lena and Alday, Josu G and Bucharova, Anna and Cancellieri, Laura and Casado-Arzuaga, Izaskun and {\v{C}}eplov{\'a}, Nat{\'a}lie and Cerver{\'o}, Llu{\"\i}sa and others},
+ journal={Basic and applied ecology},
+ volume={50},
+ pages={119--131},
+ year={2021},
+ publisher={Elsevier}
+}
+
+@article{perino2014value,
+ title={The value of urban green space in Britain: a methodological framework for spatially referenced benefit transfer},
+ author={Perino, Grischa and Andrews, Barnaby and Kontoleon, Andreas and Bateman, Ian},
+ journal={Environmental and Resource Economics},
+ volume={57},
+ pages={251--272},
+ year={2014},
+ publisher={Springer}
+}
+
+@article{hess2019apollo,
+ title={Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application},
+ author={Hess, Stephane and Palma, David},
+ journal={Journal of choice modelling},
+ volume={32},
+ pages={100170},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{bockarjova2020economic,
+ title={Economic valuation of green and blue nature in cities: A meta-analysis},
+ author={Bockarjova, Marija and Botzen, Wouter JW and Koetse, Mark J},
+ journal={Ecological Economics},
+ volume={169},
+ pages={106480},
+ year={2020},
+ publisher={Elsevier}
+}
+
+@article{stessens2020urban,
+ title={Urban green space qualities: An integrated approach towards GIS-based assessment reflecting user perception},
+ author={Stessens, Philip and Canters, Frank and Huysmans, Marijke and Khan, Ahmed Z},
+ journal={Land use policy},
+ volume={91},
+ pages={104319},
+ year={2020},
+ publisher={Elsevier}
+}
+
+@article{liu2020linking,
+ title={Linking urban air pollution with residents’ willingness to pay for greenspace: A choice experiment study in Beijing},
+ author={Liu, Zhaoyang and Hanley, Nick and Campbell, Danny},
+ journal={Journal of Environmental Economics and Management},
+ volume={104},
+ pages={102383},
+ year={2020},
+ publisher={Elsevier}
+}
+
+@article{latinopoulos2016valuing,
+ title={Valuing the benefits of an urban park project: A contingent valuation study in Thessaloniki, Greece},
+ author={Latinopoulos, Dionysis and Mallios, Zisis and Latinopoulos, Pericles},
+ journal={Land use policy},
+ volume={55},
+ pages={130--141},
+ year={2016},
+ publisher={Elsevier}
+}
+
+@article{lo2010willingness,
+ title={Willingness of residents to pay and motives for conservation of urban green spaces in the compact city of Hong Kong},
+ author={Lo, Alex Y and Jim, Chi Yung},
+ journal={Urban Forestry \& Urban Greening},
+ volume={9},
+ number={2},
+ pages={113--120},
+ year={2010},
+ publisher={Elsevier}
+}
+
+@article{haase2014quantitative,
+ title={A quantitative review of urban ecosystem service assessments: concepts, models, and implementation},
+ author={Haase, Dagmar and Larondelle, Neele and Andersson, Erik and Artmann, Martina and Borgstr{\"o}m, Sara and Breuste, J{\"u}rgen and Gomez-Baggethun, Erik and Gren, {\AA}sa and Hamstead, Zo{\'e} and Hansen, Rieke and others},
+ journal={Ambio},
+ volume={43},
+ pages={413--433},
+ year={2014},
+ publisher={Springer}
+}
+
+@article{schindler2018spatial,
+ title={Spatial sorting, attitudes and the use of green space in Brussels},
+ author={Schindler, Mirjam and Le Texier, Marion and Caruso, Geoffrey},
+ journal={Urban Forestry \& Urban Greening},
+ volume={31},
+ pages={169--184},
+ year={2018},
+ publisher={Elsevier}
+}
+
+@article{dimoudi2003vegetation,
+ title={Vegetation in the urban environment: microclimatic analysis and benefits},
+ author={Dimoudi, Argiro and Nikolopoulou, Marialena},
+ journal={Energy and buildings},
+ volume={35},
+ number={1},
+ pages={69--76},
+ year={2003},
+ publisher={Elsevier}
+}
+
+@article{tsilini2015urban,
+ title={Urban gardens as a solution to energy poverty and urban heat island},
+ author={Tsilini, Vasiliki and Papantoniou, Sotiris and Kolokotsa, Dionysia-Denia and Maria, Efpraxia-Aithra},
+ journal={Sustainable cities and society},
+ volume={14},
+ pages={323--333},
+ year={2015},
+ publisher={Elsevier}
+}
+
+@article{irga2015does,
+ title={Does urban forestry have a quantitative effect on ambient air quality in an urban environment?},
+ author={Irga, PJ and Burchett, MD and Torpy, FR},
+ journal={Atmospheric Environment},
+ volume={120},
+ pages={173--181},
+ year={2015},
+ publisher={Elsevier}
+}
+
+@article{lin2017characterization,
+ title={Characterization of particulate matter deposited on urban tree foliage: A landscape analysis approach},
+ author={Lin, Lin and Yan, Jingli and Ma, Keming and Zhou, Weiqi and Chen, Guojian and Tang, Rongli and Zhang, Yuxin},
+ journal={Atmospheric Environment},
+ volume={171},
+ pages={59--69},
+ year={2017},
+ publisher={Elsevier}
+}
+
+
+
+@Article{alpizar,
+ author={F Alpizar and F Carlsson and P Martinsson},
+ title={{Using Choice Experiments for Non-Market Valuation}},
+ journal={Economic Issues Journal Articles},
+ year=2003,
+ volume={8},
+ number={1},
+ pages={83-110},
+ keywords={}
+}
+
+@book{freeman2014measurement,
+ title={The measurement of environmental and resource values: theory and methods},
+ author={Freeman, A Myrick and Herriges, Joseph A and Kling, Catherine L},
+ year={2014},
+ publisher={Routledge}
+}
+
+@book{mariel2021environmental,
+ title={Environmental valuation with discrete choice experiments: Guidance on design, implementation and data analysis},
+ author={Mariel, Petr and Hoyos, David and Meyerhoff, J{\"u}rgen and Czajkowski, Mikolaj and Dekker, Thijs and Glenk, Klaus and Jacobsen, Jette Bredahl and Liebe, Ulf and Olsen, S{\o}ren B{\o}ye and Sagebiel, Julian and others},
+ year={2021},
+ publisher={Springer Nature}
+}
+
+@book{train2009discrete,
+ title={Discrete choice methods with simulation},
+ author={Train, Kenneth E},
+ year={2009},
+ publisher={Cambridge university press}
+}
+
+@book{hensher_rose_greene_2015, place={Cambridge}, edition={2}, title={Applied Choice Analysis}, publisher={Cambridge University Press}, author={Hensher, David A. and Rose, John M. and Greene, William H.}, year={2015}}
+
+@article{hensher2003mixed,
+ title={The mixed logit model: the state of practice},
+ author={Hensher, David A and Greene, William H},
+ journal={Transportation},
+ volume={30},
+ pages={133--176},
+ year={2003},
+ publisher={Springer}
+}
+
+@book{train2005discrete,
+ title={Discrete choice models in preference space and willingness-to-pay space},
+ author={Train, Kenneth E and Weeks, Melvyn},
+ year={2005},
+ publisher={Springer}
+}
+
+@article{johnston2010methods,
+ title={Methods, trends and controversies in contemporary benefit transfer},
+ author={Johnston, Robert J and Rosenberger, Randall S},
+ journal={Journal of Economic Surveys},
+ volume={24},
+ number={3},
+ pages={479--510},
+ year={2010},
+ publisher={Wiley Online Library}
+}
+
+@article{colombo2008can,
+ title={How can we reduce the errors from benefits transfer? An investigation using the choice experiment method},
+ author={Colombo, Sergio and Hanley, Nick},
+ journal={Land Economics},
+ volume={84},
+ number={1},
+ pages={128--147},
+ year={2008},
+ publisher={University of Wisconsin Press}
+}
+
+@article{loomis1992evolution,
+ title={The evolution of a more rigorous approach to benefit transfer: benefit function transfer},
+ author={Loomis, John B},
+ journal={Water Resources Research},
+ volume={28},
+ number={3},
+ pages={701--705},
+ year={1992},
+ publisher={Wiley Online Library}
+}
+
+@article{czajkowski2019simulation,
+ title={Simulation error in maximum likelihood estimation of discrete choice models},
+ author={Czajkowski, Miko{\l}aj and Budzi{\'n}ski, Wiktor},
+ journal={Journal of choice modelling},
+ volume={31},
+ pages={73--85},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{gamage1998size,
+ title={Size performance of some tests in one-way ANOVA},
+ author={Gamage, Jinadasa and Weerahandi, Sam},
+ journal={Communications in Statistics-Simulation and Computation},
+ volume={27},
+ number={3},
+ pages={625--640},
+ year={1998},
+ publisher={Taylor \& Francis}
+}
+
+@article{mckight2010kruskal,
+ title={Kruskal-wallis test},
+ author={McKight, Patrick E and Najab, Julius},
+ journal={The corsini encyclopedia of psychology},
+ pages={1--1},
+ year={2010},
+ publisher={Wiley Online Library}
+}
+
+@article{franke2012chi,
+ title={The chi-square test: Often used and more often misinterpreted},
+ author={Franke, Todd Michael and Ho, Timothy and Christie, Christina A},
+ journal={American journal of evaluation},
+ volume={33},
+ number={3},
+ pages={448--458},
+ year={2012},
+ publisher={Sage Publications Sage CA: Los Angeles, CA}
+}
+
+@article{JANHALL2015130,
+title = {Review on urban vegetation and particle air pollution – Deposition and dispersion},
+journal = {Atmospheric Environment},
+volume = {105},
+pages = {130-137},
+year = {2015},
+issn = {1352-2310},
+author = {Sara Janhäll},
+keywords = {Urban, Air quality, Vegetation, Deposition, Dispersion, Particle size},
+}
+
+@article{kaul2013can,
+ title={What can we learn from benefit transfer errors? Evidence from 20 years of research on convergent validity},
+ author={Kaul, Sapna and Boyle, Kevin J and Kuminoff, Nicolai V and Parmeter, Christopher F and Pope, Jaren C},
+ journal={Journal of Environmental Economics and Management},
+ volume={66},
+ number={1},
+ pages={90--104},
+ year={2013},
+ publisher={Elsevier}
+}
+
+@article{johnston2019modeling,
+ title={Modeling distance decay within valuation meta-analysis},
+ author={Johnston, Robert J and Besedin, Elena Y and Holland, Benedict M},
+ journal={Environmental and Resource Economics},
+ volume={72},
+ pages={657--690},
+ year={2019},
+ publisher={Springer}
+}
+
+@article{hess2017correlation,
+ title={Correlation and scale in mixed logit models},
+ author={Hess, Stephane and Train, Kenneth},
+ journal={Journal of choice modelling},
+ volume={23},
+ pages={1--8},
+ year={2017},
+ publisher={Elsevier}
+}
+
+@article{morrison2002choice,
+ title={Choice modeling and tests of benefit transfer},
+ author={Morrison, Mark and Bennett, Jeff and Blamey, Russell and Louviere, Jordan},
+ journal={American journal of agricultural economics},
+ volume={84},
+ number={1},
+ pages={161--170},
+ year={2002},
+ publisher={Wiley Online Library}
+}
+
+@article{bateman2011making,
+ title={Making benefit transfers work: deriving and testing principles for value transfers for similar and dissimilar sites using a case study of the non-market benefits of water quality improvements across Europe},
+ author={Bateman, Ian J and Brouwer, Roy and Ferrini, Silvia and Schaafsma, Marije and Barton, David N and Dubgaard, Alex and Hasler, Berit and Hime, Stephanie and Liekens, Inge and Navrud, St{\aa}le and others},
+ journal={Environmental and Resource Economics},
+ volume={50},
+ pages={365--387},
+ year={2011},
+ publisher={Springer}
+}
+
+@article{glenk2020spatial,
+ title={Spatial dimensions of stated preference valuation in environmental and resource economics: methods, trends and challenges},
+ author={Glenk, Klaus and Johnston, Robert J and Meyerhoff, J{\"u}rgen and Sagebiel, Julian},
+ journal={Environmental and Resource Economics},
+ volume={75},
+ pages={215--242},
+ year={2020},
+ publisher={Springer}
+}
+
+@article{czajkowski2017spatial,
+ title={Spatial heterogeneity of willingness to pay for forest management},
+ author={Czajkowski, Miko{\l}aj and Budzi{\'n}ski, Wiktor and Campbell, Danny and Giergiczny, Marek and Hanley, Nick},
+ journal={Environmental and Resource Economics},
+ volume={68},
+ pages={705--727},
+ year={2017},
+ publisher={Springer}
+}
+
+@online{GADM,
+ author = {GADM},
+ title = {{GADM} data},
+ year = 2023,
+ url = {https://gadm.org/data.html},
+ urldate = {2023-05-08}
+}
+
+@online{urban_a,
+ author = {{Copernicus Land Monitoring Services}},
+ title = {Urban atlas},
+ year = 2023,
+ url = {https://land.copernicus.eu/local/urban-atlas},
+ urldate = {2023-05-08}
+}
+
+@online{uba,
+ author = {{German Environment Agency}},
+ title = {Air data},
+ year = 2023,
+ url = {https://www.umweltbundesamt.de/en/data/air/air-data},
+ urldate = {2023-05-08}
+}
+
+@online{deutscher_wetter,
+ author = {{Deutscher Wetterdienst}},
+ title = {Parameters observed at DWD stations and partner stations on equal terms},
+ year = 2023,
+ url = {https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/monthly/climate_indices/kl/historical/},
+ urldate = {2023-05-08}
+}
+
+@article{meya2020environmental,
+ title={Environmental inequality and economic valuation},
+ author={Meya, Jasper N},
+ journal={Environmental and Resource Economics},
+ volume={76},
+ pages={235--270},
+ year={2020},
+ publisher={Springer}
+}
+
+@article{salm2023citizens,
+ title={Citizens' preferences and valuation of urban nature: Insights from two choice experiments},
+ author={Salm, JA Papineau and Bo{\v{c}}karjova, Marija and Botzen, Wouter JW and Runhaar, HAC},
+ journal={Ecological Economics},
+ volume={208},
+ pages={107797},
+ year={2023},
+ publisher={Elsevier}
+}
+
+@article{EUbiodiv,
+ title = {{EU Biodiversity Strategy for 2030: Bringing nature back into our lives}},
+ author = {{EU Commission}},
+ year = {2020}
+}
+
+@article{de2018spatial,
+ title={Spatial heterogeneity in stated preference valuation: Status, challenges and road ahead},
+ author={De Valck, Jeremy and Rolfe, John},
+ journal={International Review of Environmental and Resource Economics},
+ volume={11},
+ number={4},
+ pages={355--422},
+ year={2018},
+ publisher={Now Publishers, Inc.}
+}
+
+@article{bertram2017differences,
+ title={Differences in the recreational value of urban parks between weekdays and weekends: A discrete choice analysis},
+ author={Bertram, Christine and Meyerhoff, J{\"u}rgen and Rehdanz, Katrin and W{\"u}stemann, Henry},
+ journal={Landscape and Urban Planning},
+ volume={159},
+ pages={5--14},
+ year={2017},
+ publisher={Elsevier}
+}
+
+@online{dest_edu,
+ author = {{Destatis}},
+ title = {{Bevölkerung im Alter von 15 Jahren und mehr nach allgemeinen und beruflichen Bildungsabschlüssen nach Jahren}},
+ year = {2020},
+ url = {https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Bildungsstand/Tabellen/bildungsabschluss.html},
+ urldate = {2023-05-15}
+}
+
+@online{stat_age,
+ author = {{Statistische Ämter des Bundes und der Länder}},
+ title = {{Durchschnittsalter der Bevölkerung - Stichtag 31.12. - regionale Ebenen}},
+ year = 2021,
+ url = {https://www.regionalstatistik.de/genesis//online?operation=table&code=12411-07-01-5-B&bypass=true&levelindex=0&levelid=1624442614007#abreadcrumb},
+ urldate = {2023-05-15}
+}
+
+@online{dest_gen,
+ author = {{Destatis}},
+ title = {{Bevölkerung nach Nationalität und Geschlecht}},
+ year = {2022},
+ url = {https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/deutsche-nichtdeutsche-bevoelkerung-nach-geschlecht-deutschland.html},
+ urldate = {2023-05-15}
+}
+
+@article{lanz2013valuing,
+ title={Valuing local environmental amenity with discrete choice experiments: Spatial scope sensitivity and heterogeneous marginal utility of income},
+ author={Lanz, Bruno and Provins, Allan},
+ journal={Environmental and Resource Economics},
+ volume={56},
+ pages={105--130},
+ year={2013},
+ publisher={Springer}
+}
+
+@article{layard2008marginal,
+ title={The marginal utility of income},
+ author={Layard, Richard and Mayraz, Guy and Nickell, Stephen},
+ journal={Journal of Public Economics},
+ volume={92},
+ number={8-9},
+ pages={1846--1857},
+ year={2008},
+ publisher={Elsevier}
+}
+
+@incollection{pearson2017race,
+ title={Race, class, gender and climate change communication},
+ author={Pearson, Adam R and Ballew, Matthew T and Naiman, Sarah and Schuldt, Jonathon P},
+ booktitle={Oxford research encyclopedia of climate science},
+ year={2017}
+}
+
+@article{van2022politics,
+ title={The politics of green infrastructure: A discrete choice experiment with Flemish local decision-makers},
+ author={Van Oijstaeijen, Wito and Van Passel, Steven and Back, Phil and Cools, Jan},
+ journal={Ecological Economics},
+ volume={199},
+ pages={107493},
+ year={2022},
+ publisher={Elsevier}
+}
+
+@article{bertram2015role,
+ title={The role of urban green space for human well-being},
+ author={Bertram, Christine and Rehdanz, Katrin},
+ journal={Ecological economics},
+ volume={120},
+ pages={139--152},
+ year={2015},
+ publisher={Elsevier}
+}
+
+@article{farkas2023systematic,
+ title={A systematic review of urban green space research over the last 30 years: A bibliometric analysis},
+ author={Farkas, Jen{\H{o}} Zsolt and Hoyk, Edit and de Morais, Mariana Batista and Csom{\'o}s, Gy{\"o}rgy},
+ journal={Heliyon},
+ year={2023},
+ publisher={Elsevier}
+}
+
+@article{martin2012benefit,
+ title={Benefit transfer and spatial heterogeneity of preferences for water quality improvements},
+ author={Martin-Ortega, Julia and Brouwer, Roy and Ojea, Elena and Berbel, Julio},
+ journal={Journal of environmental management},
+ volume={106},
+ pages={22--29},
+ year={2012},
+ publisher={Elsevier}
+}
+
+@article{colombo2007testing,
+ title={Testing choice experiment for benefit transfer with preference heterogeneity},
+ author={Colombo, Sergio and Calatrava-Requena, Javier and Hanley, Nick},
+ journal={American Journal of Agricultural Economics},
+ volume={89},
+ number={1},
+ pages={135--151},
+ year={2007},
+ publisher={Wiley Online Library}
+}
+
+@online{deutscher_wetter_2,
+ author = {{Deutscher Wetterdienst}},
+ title = {Hot days},
+ year = 2023,
+ url = {https://www.dwd.de/EN/ourservices/germanclimateatlas/explanations/elements/_functions/faqkarussel/heissetage.html},
+ urldate = {2023-05-18}
+}
+
+@online{stat_income,
+ author = {{Statistische Ämter des Bundes und der Länder}},
+ title = {{Verfügbares Einkommen der privaten Haushalte einschließlich
+privater Organisationen ohne Erwerbszweck - Jahressumme -
+regionale Tiefe: Kreise und krfr. Städte}},
+ year = 2020,
+ url = {https://www.regionalstatistik.de/genesis//online?operation=table&code=82411-01-03-4&bypass=true&levelindex=0&levelid=1684576137959#abreadcrumb},
+ urldate = {2023-05-20}
+}
+
+
+@book{perman2003natural,
+ title={Natural resource and environmental economics},
+ author={Perman, Roger and Ma, Yue and McGilvray, James and Common, Michael},
+ year={2003},
+ publisher={Pearson Education}
+}
+
+@article{tunccel2014new,
+ title={A new meta-analysis on the WTP/WTA disparity},
+ author={Tun{\c{c}}el, Tuba and Hammitt, James K},
+ journal={Journal of Environmental Economics and Management},
+ volume={68},
+ number={1},
+ pages={175--187},
+ year={2014},
+ publisher={Elsevier}
+}
+
+@online{dest_pop,
+ author = {{Destatis}},
+ title = {{Kreisfreie Städte und Landkreise nach Fläche, Bevölkerung und Bevölkerungsdichte am 31.12.2021}},
+ year = {2022},
+ url = {https://www.destatis.de/DE/Themen/Laender-Regionen/Regionales/Gemeindeverzeichnis/Administrativ/04-kreise.html},
+ urldate = {2023-05-27}
+}
+
+@article{johnston2018benefit,
+ title={Benefit transfer of environmental and resource values: progress, prospects and challenges},
+ author={Johnston, Robert J and Rolfe, John and Zawojska, Ewa and others},
+ journal={International Review of Environmental and Resource Economics},
+ volume={12},
+ number={2-3},
+ pages={177--266},
+ year={2018},
+ publisher={Now Publishers, Inc.}
+}
+
+
+@article{nisbet2009,
+author = {Elizabeth K. Nisbet and John M. Zelenski and Steven A. Murphy},
+title ={The Nature Relatedness Scale: Linking Individuals' Connection With Nature to Environmental Concern and Behavior},
+
+journal = {Environment and Behavior},
+volume = {41},
+number = {5},
+pages = {715-740},
+year = {2009},
+doi = {10.1177/0013916508318748},
+
+URL = {
+
+ https://doi.org/10.1177/0013916508318748
+
+
+
+},
+eprint = {
+
+ https://doi.org/10.1177/0013916508318748
+
+
+
+}
+,
+ abstract = { Disconnection from the natural world may be contributing to our planet's destruction. The authors propose a new construct, Nature Relatedness (NR), and a scale that assesses the affective, cognitive, and experiential aspects of individuals' connection to nature. In Study 1, the authors explored the internal structure of the NR item responses in a sample of 831 participants using factor analysis. They tested the construct validity of NR with respect to an assortment of environmental and personality measures. In Study 2, they employed experience sampling methodology examining if NR people spend more time outdoors, in nature. Across studies, NR correlated with environmental scales, behavior, and frequency of time in nature, supporting the reliability and validity of NR, as well as the contribution of NR (over and above other measures) to environmental concern and behavior. The potential of NR as a useful method for investigating human-nature relationships and the processes underlying environmental concern and behaviors are discussed. }
+}
+@Inbook{mathews2007,
+author="Mathews, Kristy E.
+and Freeman, Miranda L.
+and Desvousges, William H.",
+editor="Kanninen, Barbara J.",
+title="How and How Much?",
+bookTitle="Valuing Environmental Amenities Using Stated Choice Studies: A Common Sense Approach to Theory and Practice",
+year="2007",
+publisher="Springer Netherlands",
+address="Dordrecht",
+pages="111--133",
+isbn="978-1-4020-5313-9",
+doi="10.1007/1-4020-5313-4_5",
+url="https://doi.org/10.1007/1-4020-5313-4_5 "
+}
+
+
+%julian
+
+@book{mariel2021environmental,
+ title={Environmental valuation with discrete choice experiments: Guidance on design, implementation and data analysis},
+ author={Mariel, Petr and Hoyos, David and Meyerhoff, J{\"u}rgen and Czajkowski, Mikolaj and Dekker, Thijs and Glenk, Klaus and Jacobsen, Jette Bredahl and Liebe, Ulf and Olsen, S{\o}ren B{\o}ye and Sagebiel, Julian and others},
+ year={2021},
+ publisher={Springer Nature}
+}
+
+
+@article{welling2023information,
+ title={Information processing in stated preference surveys: A case study on urban gardens},
+ author={Welling, Malte and Sagebiel, Julian and Rommel, Jens},
+ journal={Journal of Environmental Economics and Management},
+ volume={119},
+ pages={102798},
+ year={2023},
+ publisher={Elsevier}
+}
+
+
+@Article{zeileis2008,
+ title = {Regression Models for Count Data in {R}},
+ author = {Achim Zeileis and Christian Kleiber and Simon Jackman},
+ journal = {Journal of Statistical Software},
+ year = {2008},
+ volume = {27},
+ number = {8}
+ }
+
+@article{bergstrom1990impact,
+ title={The impact of information on environmental commodity valuation decisions},
+ author={Bergstrom, John C and Stoll, John R and Randall, Alan},
+ journal={American Journal of Agricultural Economics},
+ volume={72},
+ number={3},
+ pages={614--621},
+ year={1990},
+ publisher={Wiley Online Library}
+}
+
+@article{hensher1998combining,
+ title={Combining sources of preference data},
+ author={Hensher, David and Louviere, Jordan and Swait, Joffre},
+ journal={Journal of Econometrics},
+ volume={89},
+ number={1-2},
+ pages={197--221},
+ year={1998},
+ publisher={Elsevier}
+}
+
+@article{CAMPSCALVET2016,
+title = {Ecosystem services provided by urban gardens in Barcelona, Spain: Insights for policy and planning},
+journal = {Environmental Science \& Policy},
+volume = {62},
+pages = {14-23},
+year = {2016},
+note = {Advancing urban environmental governance: Understanding theories, practices and processes shaping urban sustainability and resilience},
+issn = {1462-9011},
+author = {Marta Camps-Calvet and Johannes Langemeyer and Laura Calvet-Mir and Erik Gómez-Baggethun},
+keywords = {Barcelona, Ecosystem services, Green infrastructure, Nature-based solutions, Urbanism, Urban agriculture}
+}
+
+@article{deshazo2002designing,
+ title={Designing choice sets for stated preference methods: the effects of complexity on choice consistency},
+ author={DeShazo, JR and Fermo, German},
+ journal={Journal of Environmental Economics and Management},
+ volume={44},
+ number={1},
+ pages={123--143},
+ year={2002},
+ publisher={Elsevier}
+}
+
+
+
+@book{kenski_state_2014,
+ title = {The {State} of {Framing} {Research}},
+ volume = {1},
+ abstract = {This chapter provides an assessment of the state of research on framing, and examines how the concept of framing has been used across different disciplines. Using this overview, the authors explore two of the most common conceptual misunderstandings related to framing and their implications for political communication research. This includes an overview of the different approaches to framing in political communication research, more specifically, and of the relationship between framing and related effects models, such as priming and agenda setting. The authors conclude with a recommendation that framing research be redirected away from confounded “emphasis frames†toward “equivalence frames†in the original tradition by expanding the sample of potential frames to include nonverbal visual cues.},
+ language = {en},
+ urldate = {2019-12-03},
+ publisher = {Oxford University Press},
+ author = {Scheufele, Dietram A. and Iyengar, Shanto},
+ editor = {Kenski, Kate and Jamieson, Kathleen Hall},
+ month = oct,
+ year = {2014},
+ doi = {10.1093/oxfordhb/9780199793471.013.47}
+}
+
+@article{czajkowski_controlling_2016,
+ title = {Controlling for the {Effects} of {Information} in a {Public} {Goods} {Discrete} {Choice} {Model}},
+ volume = {63},
+ issn = {0924-6460, 1573-1502},
+ language = {en},
+ number = {3},
+ urldate = {2019-12-03},
+ journal = {Environmental and Resource Economics},
+ author = {Czajkowski, Mikołaj and Hanley, Nick and LaRiviere, Jacob},
+ month = mar,
+ year = {2016},
+ pages = {523--544}
+}
+
+@article{tourangeau_cognitive_1988,
+ title = {Cognitive processes underlying context effects in attitude measurement.},
+ volume = {103},
+ language = {en},
+ number = {3},
+ journal = {Psychological Bulletin},
+ author = {Tourangeau, Roger and Rasinski, Kenneth A.},
+ year = {1988},
+ pages = {299--314}
+ }
+
+@article{liebe_context_2016,
+ title = {Context effects and the temporal stability of stated preferences},
+ volume = {60},
+ language = {en},
+ urldate = {2019-12-03},
+ journal = {Social Science Research},
+ author = {Liebe, Ulf and Hundeshagen, Cordula and Beyer, Heiko and Cramon-Taubadel, Stephan von},
+ month = nov,
+ year = {2016},
+ pages = {135--147}
+}
+
+@article{moore_measuring_2002,
+ title = {Measuring {New} {Types} of {Question}-{Order} {Effects}: {Additive} and {Subtractive}},
+ volume = {Vol. 66},
+ number = {1},
+ journal = {The Public Opinion Quarterly},
+ author = {Moore, David. W.},
+ year = {2002},
+ pages = {80--91}
+}
+
+@book{tourangeau_psychology_2000,
+ address = {Cambridge New York Melbourne},
+ title = {The psychology of survey response},
+ isbn = {978-0-521-57246-0 978-0-521-57629-1},
+ language = {eng},
+ publisher = {Cambridge University Press},
+ author = {Tourangeau, Roger and Rips, Lance J. and Rasinski, Kenneth A.},
+ year = {2000}
+}
+
+@article{pouta_attitude_2004,
+ title = {Attitude and belief questions as a source of context effect in a contingent valuation survey},
+ volume = {25},
+ issn = {01674870},
+ language = {en},
+ number = {2},
+ urldate = {2019-12-03},
+ journal = {Journal of Economic Psychology},
+ author = {Pouta, Eija},
+ month = apr,
+ year = {2004},
+ pages = {229--242}
+}
+
+@book{bateman_economic_2002,
+ title = {Economic {Valuation} with {Stated} {Preference} {Techniques}},
+ isbn = {978-1-78100-972-7},
+ language = {en},
+ urldate = {2019-12-03},
+ publisher = {Edward Elgar Publishing},
+ author = {Bateman, Ian and Carson, Richard and Day, Brett and Hanemann, Michael and Hanley, Nick and Hett, Tannis and Jones-Lee, Michael and Loomes, Graham},
+ year = {2002}
+}
+
+@article{jacobsen_whats_2008,
+ title = {What’s in a name? {The} use of quantitative measures versus ‘{Iconised}’ species when valuing biodiversity},
+ volume = {39},
+ issn = {0924-6460, 1573-1502},
+ shorttitle = {What’s in a name?},
+ language = {en},
+ number = {3},
+ urldate = {2019-12-03},
+ journal = {Environmental and Resource Economics},
+ author = {Jacobsen, Jette Bredahl and Boiesen, John Halfdan and Thorsen, Bo Jellesmark and Strange, Niels},
+ year = {2008},
+ pages = {247--263}
+}
+
+@article{johnston_contemporary_2017,
+ title = {Contemporary {Guidance} for {Stated} {Preference} {Studies}},
+ volume = {4},
+ language = {en},
+ number = {2},
+ urldate = {2019-12-03},
+ journal = {Journal of the Association of Environmental and Resource Economists},
+ author = {Johnston, Robert J. and Boyle, Kevin J. and Adamowicz, Wiktor (Vic) and Bennett, Jeff and Brouwer, Roy and Cameron, Trudy Ann and Hanemann, W. Michael and Hanley, Nick and Ryan, Mandy and Scarpa, Riccardo and Tourangeau, Roger and Vossler, Christian A.},
+ month = jun,
+ year = {2017},
+ pages = {319--405},
+ file = {Akzeptierte Version:P\:\\Zotero\\storage\\8ZC8Y7ZF\\Johnston et al. - 2017 - Contemporary Guidance for Stated Preference Studie.pdf:application/pdf}
+}
+
+@article{holmes1998effect,
+ title={The effect of response time on conjoint analysis estimates of rainforest protection values},
+ author={Holmes, Thomas and Alger, Keith and Zinkhan, Christian and Mercer, D Evan},
+ journal={Journal of Forestry Economics},
+ number = {2},
+ volume={4},
+ year={1998}
+}
+
+@article{vista2009if,
+ title={If you provide it, will they read it? Response time effects in a choice experiment},
+ author={Vista, Arvin B and Rosenberger, Randall S and Collins, Alan R},
+ journal={Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie},
+ volume={57},
+ number={3},
+ pages={365--377},
+ year={2009},
+ publisher={Wiley Online Library}
+}
+
+@article{johnston_willingness_2007,
+ title = {Willingness to {Pay} for {Agricultural} {Land} {Preservation} and {Policy} {Process} {Attributes}: {Does} the {Method} {Matter}?},
+ volume = {89},
+ issn = {0002-9092, 1467-8276},
+ shorttitle = {Willingness to {Pay} for {Agricultural} {Land} {Preservation} and {Policy} {Process} {Attributes}},
+ language = {en},
+ number = {4},
+ urldate = {2019-12-03},
+ journal = {American Journal of Agricultural Economics},
+ author = {Johnston, R. J. and Duke, J. M.},
+ month = nov,
+ year = {2007},
+ pages = {1098--1115}
+}
+
+@article{oehlmann_uncovering_2017,
+ title = {Uncovering context-induced status quo effects in choice experiments},
+ volume = {81},
+ issn = {00950696},
+ language = {en},
+ urldate = {2019-12-03},
+ journal = {Journal of Environmental Economics and Management},
+ author = {Oehlmann, Malte and Meyerhoff, Jürgen and Mariel, Petr and Weller, Priska},
+ month = jan,
+ year = {2017},
+ pages = {59--73}
+}
+
+@article{oehlmann2017stated,
+ title = {Stated preferences towards renewable energy alternatives in {Germany} – do the consequentiality of the survey and trust in institutions matter?},
+ volume = {6},
+ issn = {2160-6544, 2160-6552},
+ language = {en},
+ number = {1},
+ urldate = {2019-12-03},
+ journal = {Journal of Environmental Economics and Policy},
+ author = {Oehlmann, Malte and Meyerhoff, Jürgen},
+ month = jan,
+ year = {2017},
+ pages = {1--16}
+}
+
+@misc{andor_consequentiality_2017,
+ title = {Consequentiality and the {Willingness}-{To}-{Pay} for {Renewables}: {Evidence} from {Germany}.},
+ author = {Andor, Mark and Frondel, Manuel and Horvath, Marco},
+ month = sep,
+ year = {2017}
+}
+
+@article{carson_incentive_2007,
+ title = {Incentive and informational properties of preference questions},
+ volume = {37},
+ issn = {0924-6460, 1573-1502},
+ language = {en},
+ number = {1},
+ urldate = {2019-12-03},
+ journal = {Environmental and Resource Economics},
+ author = {Carson, Richard T. and Groves, Theodore},
+ month = may,
+ year = {2007},
+ pages = {181--210}
+}
+
+@article{ballco2019consumer,
+ title={Consumer preferences for nutritional claims: An exploration of attention and choice based on an eye-tracking choice experiment},
+ author={Ballco, Petjon and de-Magistris, Tiziana and Caputo, Vincenzina},
+ journal={Food Research International},
+ volume={116},
+ pages={37--48},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{balcombe2017examining,
+ title={Examining the relationship between visual attention and stated preferences: A discrete choice experiment using eye-tracking},
+ author={Balcombe, Kelvin and Fraser, Iain and Williams, Louis and McSorley, Eugene},
+ journal={Journal of Economic Behavior \& Organization},
+ volume={144},
+ pages={238--257},
+ year={2017},
+ publisher={Elsevier}
+}
+
+@incollection{munro_information_2001,
+ address = {Oxford},
+ series = {Oxford {University} {Press}},
+ title = {Information, uncertainty, and contingent valuation.},
+ booktitle = {Valuing {Environmental} {Preferences}: {Theory} and {Practice} of the {Contingent} {Valuation} {Method} in the {US}, {EU}, and {Developing} {Countries}},
+ author = {Munro, Alistair and Hanley, Nick},
+ year = {2001},
+ pages = {258--274}
+}
+
+@article{oh_citizens_2012,
+ title = {Citizens’ trust in government and their willingness-to-pay},
+ volume = {115},
+ issn = {01651765},
+ language = {en},
+ number = {3},
+ urldate = {2019-12-03},
+ journal = {Economics Letters},
+ author = {Oh, Hyungna and Hong, Jong Ho},
+ month = jun,
+ year = {2012},
+ pages = {345--347}
+}
+
+@book{mitchell1989,
+ title={Using surveys to value public goods: the contingent valuation method},
+ author={Mitchell, Robert Cameron and Carson, Richard T},
+ year={1989},
+ publisher={Resources for the Future}
+}
+
+@article{remoundou_is_2012,
+ title = {Is the value of an environmental public good sensitive to the providing institution?},
+ volume = {34},
+ issn = {09287655},
+ language = {en},
+ number = {3},
+ urldate = {2019-12-03},
+ journal = {Resource and Energy Economics},
+ author = {Remoundou, Kyriaki and Kountouris, Yiannis and Koundouri, Phoebe},
+ month = sep,
+ year = {2012},
+ pages = {381--395}
+}
+
+@article{needham2018causal,
+ title={What is the causal impact of information and knowledge in stated preference studies?},
+ author={Needham, Katherine and Czajkowski, Miko{\l}aj and Hanley, Nick and LaRiviere, Jacob},
+ journal={Resource and Energy Economics},
+ volume={54},
+ pages={69--89},
+ year={2018},
+ publisher={Elsevier}
+}
+
+@article{vossler_understanding_2013,
+ title = {Understanding the consequences of consequentiality: {Testing} the validity of stated preferences in the field},
+ volume = {86},
+ journal = {Journal of Economic Behavior \& Organization},
+ author = {Vossler, Christian A. and Watson, Sharon B.},
+ month = feb,
+ year = {2013},
+ pages = {137--147}
+}
+
+@article{kataria_scenario_2012,
+ title = {Scenario realism and welfare estimates in choice experiments – {A} non-market valuation study on the {European} water framework directive},
+ volume = {94},
+ language = {en},
+ number = {1},
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+@article{aadland_bayesian_2007,
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+ title = {{MEASURING} {NON}-{USE} {VALUE} {OF} {ENVIRONMENTAL} {GOODS} {USING} {THE} {CONTINGENT} {VALUATION} {METHOD}: {PROBLEMS} {OF} {INFORMATION} {AND} {COGNITION} {AND} {THE} {APPLICATION} {OF} {COGNITIVE} {QUESTIONNAIRE} {DESIGN} {METHODS}},
+ volume = {46},
+ issn = {0021857X, 14779552},
+ shorttitle = {{MEASURING} {NON}-{USE} {VALUE} {OF} {ENVIRONMENTAL} {GOODS} {USING} {THE} {CONTINGENT} {VALUATION} {METHOD}},
+ language = {en},
+ number = {1},
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+}
+
+@article{blomquist_resource_1998-1,
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+ volume = {20},
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+ language = {en},
+ number = {2},
+ urldate = {2020-01-28},
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+ file = {Submitted Version:P\:\\Zotero\\storage\\3WFSNZ8W\\Blomquist and Whitehead - 1998 - Resource quality information and validity of willi.pdf:application/pdf}
+}
+
+@article{boyle_commodity_1989,
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+ publisher={Elsevier}
+}
+
+@article{lee_information_2018,
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+ volume = {13},
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+ language = {en},
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+ urldate = {2020-01-28},
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+
+@article{hoehn2002effect,
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+}
+
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+keywords = {Forest management decision making, Soil biodiversity, Environmental valuation, Discrete choice experiment, Information treatment, Preference heterogeneity}
+}
+
+@article{hoevenagel_effects_1993,
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+}
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+}
+
+@incollection{Train2005,
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+}
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+@book{Tutz_2009,
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+
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+}
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+@article{ladenburg2014augmenting,
+ year = 2014,
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+}
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+@book{dillman2011mail,
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+}
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+@article{broadbent2012hypothetical,
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+}
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+}
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+}
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+@article{vossler2018alternative,
+ title={Alternative value elicitation formats in contingent valuation: Mechanism design and convergent validity},
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+ publisher={Elsevier}
+}
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+@article{Mariel_2016,
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+}
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+}
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+@article{Chorus_2014,
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+}
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+}
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+@article{hoehn2002,
+ title={The effect of resource quality information on resource injury perceptions and contingent values},
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+ publisher={Elsevier}
+}
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+@article{hasselstrom2014,
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+}
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+@article{zawojska_2019,
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+}
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+@article{tienhaara2020,
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+}
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+ publisher={Elsevier}
+}
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+ journal = {Marketing Letters}
+}
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+@book{greeneeconometric,
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+@article{abate2020valuation,
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+ publisher={Springer}
+}
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+@article{schmid2019store,
+ title={In-store or online shopping of search and experience goods: A hybrid choice approach},
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+ publisher={Elsevier}
+}
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+}
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+@article{golkebiowska2020energy,
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+ author={Golkebiowska, Bernadeta and Bartczak, Anna and Czajkowski, Mikolaj},
+ journal={Energies},
+ volume={13},
+ number={15},
+ pages={3779},
+ year={2020},
+ publisher={Multidisciplinary Digital Publishing Institute}
+}
+
+@book{train2009discrete,
+ title={Discrete choice methods with simulation},
+ author={Train, Kenneth E},
+ year={2009},
+ publisher={Cambridge university press}
+}
+
+@article{scarpa2008utility,
+ title={Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps},
+ author={Scarpa, Riccardo and Thiene, Mara and Train, Kenneth},
+ journal={American Journal of Agricultural Economics},
+ volume={90},
+ number={4},
+ pages={994--1010},
+ year={2008},
+ publisher={Oxford University Press}
+}
+
+
+@article{thiene2009deriving,
+ title={Deriving and testing efficient estimates of WTP distributions in destination choice models},
+ author={Thiene, Mara and Scarpa, Riccardo},
+ journal={Environmental and Resource Economics},
+ volume={44},
+ number={3},
+ pages={379},
+ year={2009},
+ publisher={Springer}
+}
+
+
+@article{daly2012using,
+ title={Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour},
+ author={Daly, Andrew and Hess, Stephane and Patruni, Bhanu and Potoglou, Dimitris and Rohr, Charlene},
+ journal={Transportation},
+ volume={39},
+ number={2},
+ pages={267--297},
+ year={2012},
+ publisher={Springer}
+}
+
+@article{raveau2012practical,
+ title={Practical and empirical identifiability of hybrid discrete choice models},
+ author={Raveau, Sebastian and Yanez, Maria Francisca and de Dios Ortuzar, Juan},
+ journal={Transportation Research Part B: Methodological},
+ volume={46},
+ number={10},
+ pages={1374--1383},
+ year={2012},
+ publisher={Elsevier}
+}
+
+
+@article{czajkowski2019simulation,
+ title={Simulation error in maximum likelihood estimation of discrete choice models},
+ author={Czajkowski, Miko{\l}aj and Budzi{\'n}ski, Wiktor},
+ journal={Journal of Choice Modelling},
+ volume={31},
+ pages={73--85},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{welling2022information,
+ title={Information, consequentiality and credibility in stated preference surveys: A choice experiment on climate adaptation},
+ author={Welling, Malte and Zawojska, Ewa and Sagebiel, Julian},
+ journal={Environmental and Resource Economics},
+ volume={82},
+ number={1},
+ pages={257--283},
+ year={2022},
+ publisher={Springer}
+}
+
+@article{stigler1961economics,
+ title={The economics of information},
+ author={Stigler, George J},
+ journal={Journal of political economy},
+ volume={69},
+ number={3},
+ pages={213--225},
+ year={1961},
+ publisher={The University of Chicago Press}
+}
+
+@article{nisbet2009nature,
+ title={The nature relatedness scale: Linking individuals' connection with nature to environmental concern and behavior},
+ author={Nisbet, Elizabeth K and Zelenski, John M and Murphy, Steven A},
+ journal={Environment and behavior},
+ volume={41},
+ number={5},
+ pages={715--740},
+ year={2009},
+ publisher={Sage Publications Sage CA: Los Angeles, CA}
+}
\ No newline at end of file