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Commit 25763cc2 authored by nc71qaxa's avatar nc71qaxa
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Presentation+Tables

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......@@ -86,3 +86,5 @@ mxl_wtp_case_c_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_
source("Scripts/visualize_models.R")
source("Scripts/compare_split_samples.R")
source("Scripts/create_tables.R")
\ No newline at end of file
......@@ -17,43 +17,69 @@ texreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_p
custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:mani",
file="Tables/ols/manipulation.tex")
# Net interview time
texreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C),
custom.model.names = c("Case A", "with Controls", "Case C", "with Controls"),
custom.header = list("Dependent variable: Net interview time" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses. Dependent variable: Net interview time.",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:net_int",
file="Tables/ols/interviewtime.tex")
# CC Time
texreg(l=list(ols_time_cc_A, ols_time_cc_control_A, ols_time_cc_C, ols_time_cc_control_C),
custom.model.names = c("Case A", "with Controls", "Case C", "with Controls"),
custom.header = list("Dependent variable: Mean choice card time" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses. Dependent variable: Mean choice card time.",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:cctime",
file="Tables/ols/cctime.tex")
# Consequentiality
texreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_model_control_C),
custom.model.names = c("Case A", "with Controls", "Case C", "with Controls"),
custom.header = list("Dependent variable: Consequentiality score" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses. Dependent variable: Consequentiality score.",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:conseq",
file="Tables/ols/consequentiality.tex")
# Opt Out
texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
custom.model.names = c("Case A", "with Controls", "Case C", "with Controls"),
custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses. Dependent variable: Number of opt-out choices.",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:optout",
file="Tables/ols/optout.tex")
# NR
texreg(l=list(nr_model_treat_A),
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",
file="Tables/ols/nr_ols.tex")
#### Logit #####
texreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.header = list("Voluntary Information Access" = 1),
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",
file="Tables/logit/chose_treatment.tex")
......@@ -75,11 +101,29 @@ texreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
"wd" = "Walking Distance", "asc" = "ASC SQ"),
custom.model.names = c("Mean", "SD", "Treated", "Voluntary Treated"), custom.note = "%stars. Standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_A",
file="Tables/mxl/case_A_rent_INT.tex")
### Baseline case C
case_C <- quicktexregapollo(mxl_wtp_case_c_rentINT)
coef_names <- case_C@coef.names
coef_names <- sub("^(mu_)(.*)(1|2|info)$", "\\2\\3", coef_names)
coef_names[4] <- "mu_ASC_sq"
case_C@coef.names <- coef_names
case_C_cols <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$"), subcoef, case_C)
texreg(c(case_C_cols[1], remGOF(case_C_cols[2:7])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
"ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
"wd" = "Walking Distance", "asc" = "ASC SQ",
"ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info"), custom.note = "%stars. Standard errors in parentheses.",
stars = c(0.01, 0.05, 0.1), float.pos="tb",
label = "tab:mxl_C",
file="Tables/mxl/case_C_rent_INT.tex")
# Main model
# texreg(l=list(mxl_wtp_case_a_rentINT),
......
......@@ -15,8 +15,8 @@ nr_model_treat <- lm(Z_Mean_NR ~ Age_mean + Uni_degree + Kids_Dummy + Gender_fem
summary(nr_model_treat)
nr_model_treat_A <- lm(Z_Mean_NR ~ Age_mean + Uni_degree + Kids_Dummy + Gender_female+ QFIncome +
as.factor(Treatment_A) + Naturalness_SQ, data)
nr_model_treat_A <- lm(Z_Mean_NR ~ as.factor(Treatment_A) + QFIncome + as.factor(Gender)+Age_mean+Uni_degree + Kids_Dummy +
Naturalness_SQ + WalkingDistance_SQ, data)
summary(nr_model_treat_A)
......
---
title: "Hot Topic, Cool Choices?"
subtitle: "How information treatments on urban heat islands affect WTP for urban green spaces in Germany"
subtitle: "The Impact of Mandatory vs. Voluntary Treatments on Green Space Valuation"
title-slide-attributes:
data-background-image: Grafics/iDiv_logo_item.png
data-background-size: contain
......@@ -15,6 +15,7 @@ format:
smaller: true
logo: Grafics/iDiv_logo_item.PNG
scrollable: true
embed-resources: true
---
```{r, include=FALSE, cache=FALSE}
......@@ -24,6 +25,7 @@ source("Scripts/MAKE_FILE.R")
```{r loadlibs, include=FALSE}
library(tidyverse)
library(apollo)
library(texreg)
```
## Motivation
......@@ -34,15 +36,16 @@ library(apollo)
- We employ DCE to test influence of additional information on urban heat island on the valuation of UGS
**Research questions:**
## Research questions
1. How does an information treatment about urban heat islands affect survey engagement (interview time, cc time), quiz questions, consequentially and NR-Index?
2. How does additional information on urban heat islands affect the willingness to pay for UGS in a discrete choice experiment?
2. How do the different treatments affect the WTP for urban green spaces in the choice experiment?
3. Who chooses optional information?
4. Do people who choose voluntary information have a different WTP/preferences?
4. Do people who choose **voluntary** information have a different WTP/preferences?
## Discrete Choice Experiment
......@@ -50,6 +53,7 @@ library(apollo)
- Main attribute of interest here: naturalness defined by five-level graphical scale ▶ Range: hardly natural to very natural
- Three survey rounds; paper by Bronnmann et al. (2023) based on round 1 & 2, round 3 just finished end of February
## Choice Card
![](images/Figure%202.PNG){width="300"}
......@@ -78,9 +82,15 @@ library(apollo)
## Methods
- OLS and Logit regressions
- OLS and Logit regressions:
- Mixed logit model with interactions:
```{=tex}
\begin{equation}
Y = \beta_0 + \beta_{Treat} \cdot v_{Treat} + \beta_{Control} \cdot v_{Control} + \epsilon
\label{ols}
\end{equation}
```
- Mixed logit model with interactions in WTP space:
```{=tex}
\begin{equation}
......@@ -111,8 +121,11 @@ datatable(treatment_socio_C)
:::
:::
## NR OLS
**Hypotheses:** Individuals with greater Nature Relatedness (NR) are more inclined to autonomously seek information about environmental subjects, such as the impact of urban green spaces on urban heat islands. Consequently, any observed increase in the willingness to pay among the treated group may be attributed to the individuals' higher NR rather than the treatment itself.
```{r}
summary(nr_model_treat_A)
```
......@@ -122,16 +135,18 @@ summary(nr_model_treat_A)
Characteristics of the voluntarily treated persons
```{r}
summary(logit_choice_treat)
summary(logit_choice_treat_uni)
```
## Logit Regression: "Protest voting"
<!-- ## Logit Regression: "Protest voting" -->
Does treatment affect "protest" voting?
<!-- Does treatment affect "protest" voting? -->
```{r}
summary(logit_choice_prot_tr)
```
<!-- ```{r} -->
<!-- summary(logit_choice_prot_tr) -->
<!-- ``` -->
## Engagement: Interview Time
......@@ -415,8 +430,6 @@ summary(ols_opt_out_control_C)
```
:::
## OLS: Consequentiality
with controls
......@@ -476,33 +489,37 @@ summary(mxl_wtp_case_c_rentINT)
```
:::
## MXL: WTP space without protesters
<!-- ## MXL: WTP space without protesters -->
As protesting is not affected by the treatment we might see a treatment affect removing the protesters, which always choose opt-out.
<!-- As protesting is not affected by the treatment we might see a treatment affect removing the protesters, which always choose opt-out. -->
::: panel-tabset
### Scenario A
<!-- ::: panel-tabset -->
```{r}
summary(mxl_wtp_case_a_prot)
```
<!-- ### Scenario A -->
### Scenario B
<!-- ```{r} -->
```{r}
summary(mxl_wtp_case_b_prot)
```
<!-- summary(mxl_wtp_case_a_prot) -->
### Scenario C
<!-- ``` -->
```{r}
summary(mxl_wtp_case_c_prot)
```
:::
<!-- ### Scenario B -->
## NR Index
<!-- ```{r} -->
**Hypotheses:** Individuals with greater Nature Relatedness (NR) are more inclined to autonomously seek information about environmental subjects, such as the impact of urban green spaces on urban heat islands. Consequently, any observed increase in the willingness to pay among the treated group may be attributed to the individuals' higher NR rather than the treatment itself.
<!-- summary(mxl_wtp_case_b_prot) -->
<!-- ``` -->
<!-- ### Scenario C -->
<!-- ```{r} -->
<!-- summary(mxl_wtp_case_c_prot) -->
<!-- ``` -->
<!-- ::: -->
## MXL: WTP space
......@@ -528,100 +545,100 @@ summary(mxl_wtp_case_c_NR)
```
:::
## MXL: WTP space
<!-- ## MXL: WTP space -->
::: panel-tabset
### Scenario A
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
```{r}
apollo_modelOutput(mxl_wtp_case_a)
```
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_a) -->
<!-- ``` -->
### Scenario B
<!-- ### Scenario B -->
```{r}
apollo_modelOutput(mxl_wtp_case_b)
```
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_b) -->
<!-- ``` -->
### Scenario C
<!-- ### Scenario C -->
```{r}
apollo_modelOutput(mxl_wtp_case_c)
```
:::
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_c) -->
<!-- ``` -->
<!-- ::: -->
## MXL: WTP space Graphs
<!-- ## MXL: WTP space Graphs -->
::: panel-tabset
### Scenario A
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
::: panel-tabset
### Naturalness
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
```{r}
wtp_nat_a
```
<!-- ```{r} -->
<!-- wtp_nat_a -->
<!-- ``` -->
### Walking Distance
<!-- ### Walking Distance -->
```{r}
wtp_wd_a
```
:::
<!-- ```{r} -->
<!-- wtp_wd_a -->
<!-- ``` -->
<!-- ::: -->
### Scenario B
<!-- ### Scenario B -->
::: panel-tabset
### Naturalness
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
```{r}
wtp_nat_b
```
<!-- ```{r} -->
<!-- wtp_nat_b -->
<!-- ``` -->
### Walking Distance
<!-- ### Walking Distance -->
```{r}
wtp_wd_b
```
:::
<!-- ```{r} -->
<!-- wtp_wd_b -->
<!-- ``` -->
<!-- ::: -->
### Scenario C
<!-- ### Scenario C -->
::: panel-tabset
### Naturalness
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
```{r}
wtp_nat_c
```
<!-- ```{r} -->
<!-- wtp_nat_c -->
<!-- ``` -->
### Walking Distance
<!-- ### Walking Distance -->
```{r}
wtp_wd_c
```
:::
:::
<!-- ```{r} -->
<!-- wtp_wd_c -->
<!-- ``` -->
<!-- ::: -->
<!-- ::: -->
## MXL: WTP space
<!-- ## MXL: WTP space -->
with NR index
<!-- with NR index -->
::: panel-tabset
### Scenario A
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
```{r}
apollo_modelOutput(mxl_wtp_case_a_NR)
```
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_a_NR) -->
<!-- ``` -->
### Scenario B
<!-- ### Scenario B -->
```{r}
apollo_modelOutput(mxl_wtp_case_b_NR)
```
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_b_NR) -->
<!-- ``` -->
### Scenario C
<!-- ### Scenario C -->
```{r}
apollo_modelOutput(mxl_wtp_case_c_NR)
```
:::
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_c_NR) -->
<!-- ``` -->
<!-- ::: -->
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