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Commit 35894914 authored by nc71qaxa's avatar nc71qaxa
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conclusion

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......@@ -53,6 +53,6 @@ plot_cons_C <- create_interaction_term_plot(conseq_model_control_C, case_C_label
"Consequentiality Score", -0.5, 0.8)
plot_opt_A <- create_interaction_term_plot(ols_opt_out_control_A, case_A_labels, case_A_labels,
"Number of Opt-out Choices", -1.5, 1)
"Number of Status Quo Choices", -1.5, 1)
plot_opt_C <- create_interaction_term_plot(ols_opt_out_control_C, case_C_labels, case_C_labels_re,
"Number of Opt-out Choices", -1.5, 1)
"Number of Status Quo Choices", -1.5, 1)
......@@ -148,9 +148,9 @@ To what extent do you agree or disagree with the following statements?
- Timings: We saved the net interview time and the mean Choice Card time.-\> **Survey engagement**
- **Consequentiality**:
-- To what extent do you believe that the decisions you make will have an impact on how the green spaces in your neighbourhood are designed in the future?
- 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?
-- To what extent do you believe that the decisions you make will affect whether you have to pay a contribution for urban greening in the future?
- To what extent do you believe that the decisions you make will affect whether you have to pay a contribution for urban greening in the future?
:::
## Methods (1) {auto-animate="true"}
......@@ -337,7 +337,7 @@ htmlreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_mo
:::
:::
## OLS: Opt-out
## OLS: Status quo
::: panel-tabset
### Plot
......@@ -352,27 +352,16 @@ ggpubr::ggarrange(plot_opt_A, plot_opt_C)
```{r, results='asis'}
htmlreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
custom.header = list("Dependent variable: Number of status quo choices" = 1:4),
custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
custom.note = "%stars. Standard errors in parentheses.",
label = "tab:optout", single.row = TRUE,
caption = "Results of OLS on number of opt-out choices.")
caption = "Results of OLS on number of status quo choices.")
```
:::
:::
## 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))
```
## MXL: Effects on stated preferences
......@@ -469,12 +458,28 @@ htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
::: incremental
- Respondents that voluntary access information do engage differently in the survey
- Respondents that voluntary access information do engage more in the survey & have a higher consequentiality score
- Voluntary information access is negatively correlated with number of status quo choices
- Higher willingness to pay values in groups that voluntary access information
:::
## Conclusion
::: incremental
- Obligatory and voluntary information treatments increase information recall and willingness to pay for naturalness of and proximity to urban green spaces
- Exogenous treatments do not affect consequentiality
- Voluntary information access is correlated with increased consequentiality, higher survey engagement and higher willingness to pay
- Obligatory information treatment is more effective
:::
## Appendix
Information provision (Video) Link to the video: https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4
......@@ -488,23 +493,23 @@ Information provision (Video) Link to the video: https://idiv.limequery.com/uplo
![](Grafics/sum_b_2.png){width="300"}
## Socio Demografics {.smaller}
<!-- ## Socio Demografics {.smaller} -->
::: {style="font-size: 50%;"}
::: panel-tabset
### Case A
<!-- ::: {style="font-size: 50%;"} -->
<!-- ::: panel-tabset -->
<!-- ### Case A -->
```{r}
kableExtra::kable(treatment_socio_A)
```
<!-- ```{r} -->
<!-- kableExtra::kable(treatment_socio_A) -->
<!-- ``` -->
### Case B
<!-- ### Case B -->
```{r}
kableExtra::kable(treatment_socio_C)
```
:::
:::
<!-- ```{r} -->
<!-- kableExtra::kable(treatment_socio_C) -->
<!-- ``` -->
<!-- ::: -->
<!-- ::: -->
## NR OLS
......@@ -526,136 +531,16 @@ htmlreg(l=list(nr_model_treat_A), single.row = TRUE,
```
:::
<!-- ## MXL: WTP space -->
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_a) -->
<!-- ``` -->
<!-- ### Scenario B -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_b) -->
<!-- ``` -->
<!-- ### Scenario C -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_c) -->
<!-- ``` -->
<!-- ::: -->
<!-- ## MXL: WTP space Graphs -->
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
<!-- ```{r} -->
<!-- wtp_nat_a -->
<!-- ``` -->
<!-- ### Walking Distance -->
<!-- ```{r} -->
<!-- wtp_wd_a -->
<!-- ``` -->
<!-- ::: -->
<!-- ### Scenario B -->
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
<!-- ```{r} -->
<!-- wtp_nat_b -->
<!-- ``` -->
<!-- ### Walking Distance -->
<!-- ```{r} -->
<!-- wtp_wd_b -->
<!-- ``` -->
<!-- ::: -->
<!-- ### Scenario C -->
<!-- ::: panel-tabset -->
<!-- ### Naturalness -->
<!-- ```{r} -->
<!-- wtp_nat_c -->
<!-- ``` -->
<!-- ### Walking Distance -->
<!-- ```{r} -->
<!-- wtp_wd_c -->
<!-- ``` -->
<!-- ::: -->
<!-- ::: -->
<!-- ## MXL: WTP space -->
<!-- with NR index -->
<!-- ::: panel-tabset -->
<!-- ### Scenario A -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_a_NR) -->
<!-- ``` -->
<!-- ### Scenario B -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_b_NR) -->
<!-- ``` -->
<!-- ### Scenario C -->
<!-- ```{r} -->
<!-- apollo_modelOutput(mxl_wtp_case_c_NR) -->
## 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))
```
<!-- ::: -->
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