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presentation_tables

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......@@ -26,6 +26,11 @@ source("Scripts/MAKE_FILE.R")
library(tidyverse)
library(apollo)
library(texreg)
list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
"as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
"as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
"Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
```
## Motivation
......@@ -38,11 +43,13 @@ library(texreg)
## 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?
1. Who chooses optional information?
2a. How does an information treatment about urban heat islands affect survey engagement (interview time, cc time), quiz questions, and consequentially?
2. How do the different treatments affect the WTP for urban green spaces in the choice experiment?
2b. How are these factors influenced by voluntary information access?
3. Who chooses optional information?
3. How do the different treatments affect the WTP for urban green spaces in the choice experiment?
4. Do people who choose **voluntary** information have a different WTP/preferences?
......@@ -122,27 +129,39 @@ datatable(treatment_socio_C)
:::
## NR OLS
## NR
**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)
## NR OLS
::: {style="font-size: 50%;"}
```{r, results='asis'}
htmlreg(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")
```
:::
## Logit Regression: Who choses treatment?
::: {style="font-size: 50%;"}
Characteristics of the voluntarily treated persons
```{r, results='asis'}
list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
"as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
"as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
"Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.model.names = c("Logit regression"),
......@@ -155,15 +174,7 @@ htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="t
:::
<!-- ## Logit Regression: "Protest voting" -->
<!-- Does treatment affect "protest" voting? -->
<!-- ```{r} -->
<!-- summary(logit_choice_prot_tr) -->
<!-- ``` -->
## Engagement: Interview Time
......@@ -189,49 +200,19 @@ bxplt_interview_time_C
## OLS Engagement: Interview time
::: panel-tabset
### Scenario A
```{r}
summary(ols_time_spent_A)
```
### Scenario B
```{r}
summary(ols_time_spent_B)
::: {style="font-size: 50%;"}
```{r, results='asis'}
htmlreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C),
custom.model.names = c("Case A", "with Controls", "Case 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.",
label = "tab:net_int")
```
### Scenario C
```{r}
summary(ols_time_spent_C)
```
:::
## OLS Engagement: Interview time
with controls
::: panel-tabset
### Scenario A
```{r}
summary(ols_time_spent_control_A)
```
### Scenario B
```{r}
summary(ols_time_spent_control_B)
```
### Scenario C
```{r}
summary(ols_time_spent_control_C)
```
:::
## Engagement: Choice Card time
......@@ -255,50 +236,18 @@ bxplt_cc_time_C
```
:::
## OLS Engagement: Choice Card time
::: panel-tabset
### Scenario A
```{r}
summary(ols_time_cc_A)
```
### Scenario B
```{r}
summary(ols_time_cc_B)
```
### Scenario C
```{r}
summary(ols_time_cc_C)
```
:::
## OLS Engagement: Choice Card time
with controls
::: panel-tabset
### Scenario A
```{r}
summary(ols_time_cc_control_A)
```
### Scenario B
## OLS Engagement: Choice Card Time
```{r}
summary(ols_time_cc_control_B)
::: {style="font-size: 50%;"}
```{r, results='asis'}
htmlreg(l=list(ols_time_cc_A, ols_time_cc_control_A, ols_time_cc_C, ols_time_cc_control_C),
custom.model.names = c("Case A", "with Controls", "Case 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.",
label = "tab:cctime")
```
### Scenario C
```{r}
summary(ols_time_cc_control_C)
```
:::
## Manipulation check
......@@ -325,59 +274,44 @@ bxplt_quiz_C
## OLS: Manipulation check
::: panel-tabset
### Group A
```{r}
summary(ols_percentage_correct_A)
```
### Group B
::: {style="font-size: 50%;"}
```{r}
summary(ols_percentage_correct_B)
```{r, results='asis'}
htmlreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_percentage_correct_C, ols_percentage_correct_control_C),
custom.model.names = c("Case A", "with Controls", "Case C", "with Controls"),
custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:mani")
```
### Group C
```{r}
summary(ols_percentage_correct_C)
```
:::
## OLS: Manipulation check
<!-- ## Self Reference -->
with controls
<!-- 1. Es entspricht meiner persönlichen Erfahrung, dass die Grünfläche in meiner Nähe zu einem angenehmen Klima an meinem Wohnort beiträgt. -->
<!-- 2. Ich bin durch hohe Temperaturen in der Stadt im Sommer eingeschränkt. -->
<!-- 3. Die Stadt sollte mehr unternehmen, um Hitzeinseln zu vermeiden. -->
::: panel-tabset
### Group A
<!-- Stimme voll und ganz zu - Stimme gar nicht zu -->
```{r}
summary(ols_percentage_correct_control_A)
```
<!-- **Only the treated participants got these questions!** -->
### Group B
## OLS: Consequentiality
```{r}
summary(ols_percentage_correct_control_B)
```
### Group C
::: {style="font-size: 50%;"}
```{r}
summary(ols_percentage_correct_control_C)
```{r, results='asis'}
htmlreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_model_control_C),
custom.model.names = c("Case A", "with Controls", "Case 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.",
label = "tab:conseq")
```
:::
## Self Reference
1. Es entspricht meiner persönlichen Erfahrung, dass die Grünfläche in meiner Nähe zu einem angenehmen Klima an meinem Wohnort beiträgt.
2. Ich bin durch hohe Temperaturen in der Stadt im Sommer eingeschränkt.
3. Die Stadt sollte mehr unternehmen, um Hitzeinseln zu vermeiden.
Stimme voll und ganz zu - Stimme gar nicht zu
**Only the treated participants got these questions!**
:::
## Opt Out
......@@ -403,73 +337,21 @@ bxplt_opt_C
## OLS: Opt-out
::: panel-tabset
### Group A
```{r}
summary(ols_opt_out_A)
```
### Group B
```{r}
summary(ols_opt_out_B)
```
### Group C
```{r}
summary(ols_opt_out_C)
```
:::
## OLS: Opt-out
with controls
::: panel-tabset
### Group A
```{r}
summary(ols_opt_out_control_A)
```
### Group B
::: {style="font-size: 50%;"}
```{r}
summary(ols_opt_out_control_B)
```{r, results='asis'}
htmlreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
custom.model.names = c("Case A", "with Controls", "Case 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.",
label = "tab:optout")
```
### Group C
```{r}
summary(ols_opt_out_control_C)
```
:::
## OLS: Consequentiality
with controls
::: panel-tabset
### Group A
```{r}
summary(conseq_model_control_A)
```
### Group B
```{r}
summary(conseq_model_control_B)
```
### Group C
```{r}
summary(conseq_model_control_C)
```
:::
## MXL: Split Samples
......@@ -572,7 +454,7 @@ summary(mxl_wtp_case_c_rentINT)
<!-- ::: -->
## MXL: WTP space with NR index (new)
## MXL: WTP space with NR index
......@@ -592,29 +474,7 @@ summary(mxl_wtp_NR_case_c_rentINT)
:::
## MXL: WTP space with NR index (old)
::: panel-tabset
### Scenario A
```{r}
summary(mxl_wtp_case_a_NR)
```
### Scenario B
```{r}
summary(mxl_wtp_case_b_NR)
```
### Scenario C
```{r}
summary(mxl_wtp_case_c_NR)
```
:::
## Case D
......
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