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Commit f7da79d1 authored by nc71qaxa's avatar nc71qaxa
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lua filter appendix slides

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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
......@@ -14,6 +14,7 @@ date-format: long
bibliography: references.bib
filters:
- parse-latex
- custom_app.lua
format:
revealjs:
slide-number: true
......@@ -32,10 +33,13 @@ 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" = "Optional Treatment",
"as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
"as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
"as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
"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")
```
......@@ -44,18 +48,18 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## 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.
- **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.
- 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
......@@ -64,19 +68,19 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## 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.
- 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.
- 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
......@@ -100,11 +104,11 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## Discrete Choice Experiment
::: incremental
- To investigate the research questions, we use data from a **discrete choice experiment (DCE)** on naturalness of urban green spaces.
- 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**.
- 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**. -->
:::
......@@ -114,15 +118,15 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
## 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.
- Info text about the effect of **natural urban green spaces** on urban **heat islands**
- **Optional video** (2 minutes) with the almost the same information
![](images/waermeinsel.png){width="200"}
## Treatment (Quiz & Self Reference Questions)
::: incremental
- **Seven quiz questions** with strict reference to the previously provided information.
- **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)*
......@@ -149,7 +153,7 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
- 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.
+ **Socio-demographics**: Age, Gender, Income, Education
+ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness** [@nisbet2009nature]
......@@ -320,9 +324,9 @@ with
### Results
::: {style="font-size: 68%;"}
::: {style="font-size: 90%;"}
```{r, results='asis'}
htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
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"),
......@@ -476,7 +480,8 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
::: {#refs}
:::
# Appendix
# Appendix {.appendix}
Information provision (Video) Link to the video: <https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4>
......@@ -534,6 +539,51 @@ htmlreg(l=list(nr_model_treat_A), single.row = TRUE,
```
:::
## 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}
......
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