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Include Bibliography

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_extensions/tarleb/parse-latex/_extension.yml 0 → 100644
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name: parse-latex
author: Albert Krewinkel
version: 1.0.0
contributes:
filters:
- parse-latex.lua
_extensions/tarleb/parse-latex/parse-latex.lua 0 → 100644
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--- parse-latex.lua – parse and replace raw LaTeX snippets
---
--- Copyright: © 2021–2022 Albert Krewinkel
--- License: MIT – see LICENSE for details
-- Makes sure users know if their pandoc version is too old for this
-- filter.
PANDOC_VERSION:must_be_at_least '2.9'
-- Return an empty filter if the target format is LaTeX: the snippets will be
-- passed through unchanged.
if FORMAT:match 'latex' then
return {}
end
-- Parse and replace raw TeX blocks, leave all other raw blocks
-- alone.
function RawBlock (raw)
if raw.format:match 'tex' then
return pandoc.read(raw.text, 'latex').blocks
end
end
-- Parse and replace raw TeX inlines, leave other raw inline
-- elements alone.
function RawInline(raw)
if raw.format:match 'tex' then
return pandoc.utils.blocks_to_inlines(
pandoc.read(raw.text, 'latex').blocks
)
end
end
project_start.qmd
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...@@ -11,6 +11,9 @@ institute: ...@@ -11,6 +11,9 @@ institute:
- Leipzig University - Leipzig University
date: today date: today
date-format: long date-format: long
bibliography: references.bib
filters:
- parse-latex
format: format:
revealjs: revealjs:
slide-number: true slide-number: true
...@@ -53,25 +56,25 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = ...@@ -53,25 +56,25 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
- Too **much information** may increase survey **complexity**, leading to respondents being overburdened with it and producing less consistent choices. - 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**. - 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. - 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.
- **Voluntary information** allows the respondents to gather required information if needed and might increase efficiency of information provision - **Optional information** allows the respondents to gather required information if needed and might increase efficiency of information provision
::: :::
## Literature ## Literature
::: incremental ::: incremental
- There is **little research** on the effects of **optional information provision** on choice behavior and information recall. - There is **little research** on the effects of **optional information provision** on choice behavior and information recall.
- In their study, **Tienhaara et al. (2022)** surveyed preferences for agricultural genetic resources, allowing respondents the option to access detailed information on the valued goods prior to preference elicitation. - 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, **Hu et al. (2009)** offered respondents the opportunity to access voluntary information about genetic modified food before participating in a choice experiment. - Similarly, @hu2009consumers offered respondents the opportunity to access voluntary information about genetic modified food before participating in a choice experiment.
- Both studies conclude that, on average, respondents who retrieve voluntary information have **larger willingness to pay** for the good to be valued. - Both studies conclude that, on average, respondents who retrieve voluntary information have **larger willingness to pay** for the good to be valued.
- Their study design, however, does not allow comparing the voluntary information retrieval to a version where the additional information was shown obligatory. - Their study design, however, does not allow comparing the optional information retrieval to a version where the additional information was shown obligatory.
::: :::
## Research Contribution ## Research Contribution
::: incremental ::: incremental
- Our study explores the impact of additional obligatory and optional information on stated preferences using an exogenous split sample approach with three treatments. - 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 Welling et al. (2023), expanding our understanding of treatment effects. - 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 choose additional information and to what extent they have different preferences than respondents who do not choose additional information. - We test who chooses additional information and to what extent their preferences differ from those who do not choose additional information.
::: :::
## Research Questions ## Research Questions
...@@ -88,7 +91,7 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = ...@@ -88,7 +91,7 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
::: incremental ::: incremental
- To investigate the research questions, we use data from a **discrete choice experiment (DCE)** on naturalness of urban green spaces. - 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 **Bronnmann et al., (2023)** and differs only in the information provided to the respondents. - 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**. - 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**. - 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**. - The associated **costs** of this restructuring were intended to be integrated into monthly **rental payments**.
...@@ -99,38 +102,23 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = ...@@ -99,38 +102,23 @@ list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" =
![](images/Figure%202.PNG){width="300"} ![](images/Figure%202.PNG){width="300"}
## Treatment (Information provision) ## Treatment (Information Provision)
- Short info text about the effect of **natural urban green spaces** on urban **heat islands**. - Short info text about the effect of **natural urban green spaces** on urban **heat islands**.
- **Optional video** with the almost the same information. - **Optional video** with the almost the same information.
![](images/waermeinsel.png){width="200"} ![](images/waermeinsel.png){width="200"}
## Treatment (Quiz) ## Treatment (Quiz & Self Reference Questions)
::: incremental ::: incremental
**Seven quiz questions** with strict reference to the previously provided information. - **Seven quiz questions** with strict reference to the previously provided information.
Example Questions: - Example: *The temperature difference between the city and the surrounding area can be up to 10 degrees Celsius. (true/false)*
1. Which of the following statements are correct? - **Two self reference questions**
- The temperature difference between the city and the surrounding area can be up to 10 degrees Celsius. (true/false) - Example: *The city should do more to avoid heat islands. (Strongly agree - Strongly disagree)*
2. According to the information provided, which of the following properties influences the temperature in the city?
- The proximity of green spaces to nature (yes/no)
- Light pollution in the city (yes/no)
:::
## Treatment (Self reference)
::: incremental
To what extent do you agree or disagree with the following statements?
1. I am limited by high temperatures in the city during the summer. (Strongly agree - Strongly disagree)
2. The city should do more to avoid heat islands. (Strongly agree - Strongly disagree)
::: :::
## Experimental Setting ## Experimental Setting
...@@ -150,14 +138,19 @@ To what extent do you agree or disagree with the following statements? ...@@ -150,14 +138,19 @@ To what extent do you agree or disagree with the following statements?
::: incremental ::: incremental
+ Quiz: Evaluation of the quiz we gave to everyone after the DCE.**-\>Information recall** + 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** + Timings: We saved the net interview time and the mean Choice Card time.-\> **Survey engagement**
+ **Consequentiality**: + **Consequentiality**:
1. 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 ) 1. 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 )
2. 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? (I believe in it very much - I don’t believe in it at all ) 2. 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? (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**.
+ **Attitudinal variable**: Measure derived from 21 items on **nature relatedness** (@nisbet2009nature)
::: :::
# Methods # Methods
...@@ -197,7 +190,6 @@ To what extent do you agree or disagree with the following statements? ...@@ -197,7 +190,6 @@ To what extent do you agree or disagree with the following statements?
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} + \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} \end{equation}
``` ```
with with
```{=tex} ```{=tex}
...@@ -212,58 +204,48 @@ and ...@@ -212,58 +204,48 @@ and
v_{Treat_A} = \{Treated, Optional Treatment\} v_{Treat_A} = \{Treated, Optional Treatment\}
\end{equation} \end{equation}
``` ```
```{=tex} ```{=tex}
\begin{equation} \begin{equation}
v_{Treat_B} = \{Treated, Vol. Treated, No Info\} v_{Treat_B} = \{Treated, Vol. Treated, No Info\}
\end{equation} \end{equation}
``` ```
# Case A: Obligatory vs. Optional Information
# Case A: Obligatory vs. optional information
<!-- ::: {style="font-size: 45%;"} --> <!-- ::: {style="font-size: 45%;"} -->
<!-- ```{r, results='asis'} -->
<!-- ```{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), --> <!-- 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.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.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.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb", -->
<!-- label = "tab:olsA", --> <!-- label = "tab:olsA", -->
<!-- caption = "Results of OLS regressions for Scenario Case A.") --> <!-- caption = "Results of OLS regressions for Scenario Case A.") -->
<!-- ``` -->
<!-- ::: -->
<!-- ``` -->
<!-- ::: -->
## OLS Engagement: Interview & Choice Card Time ## OLS Engagement: Interview & Choice Card Time
```{r} ```{r}
ggpubr::ggarrange(plot_interview_A, plot_cc_A) ggpubr::ggarrange(plot_interview_A, plot_cc_A)
``` ```
## OLS: Information Recall & Consequentiality
## OLS: Information recall & Consequentiality
```{r} ```{r}
ggpubr::ggarrange(plot_mani_A, plot_cons_A) ggpubr::ggarrange(plot_mani_A, plot_cons_A)
``` ```
## MXL: Effects on Stated Preferences
## MXL: Effects on stated preferences
::: {style="font-size: 60%;"} ::: {style="font-size: 60%;"}
```{r, results='asis'} ```{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])),
custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent", custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
...@@ -276,11 +258,9 @@ htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])), ...@@ -276,11 +258,9 @@ htmlreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
``` ```
::: :::
# Case B: Voluntary Information Access
## Logit Regression: Who chooses Treatment?
# Case B: Voluntary information access
## Logit Regression: Who choses treatment?
::: {style="font-size: 65%;"} ::: {style="font-size: 65%;"}
```{r, results='asis'} ```{r, results='asis'}
...@@ -299,15 +279,11 @@ htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="t ...@@ -299,15 +279,11 @@ htmlreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="t
## OLS Engagement: Interview & Choice Card Time ## OLS Engagement: Interview & Choice Card Time
```{r} ```{r}
ggpubr::ggarrange(plot_interview_D, plot_cc_D) ggpubr::ggarrange(plot_interview_D, plot_cc_D)
``` ```
## OLS: Information Recall & Consequentiality
## OLS: Information recall & Consequentiality
```{r} ```{r}
ggpubr::ggarrange(plot_mani_D, plot_cons_D) ggpubr::ggarrange(plot_mani_D, plot_cons_D)
...@@ -329,7 +305,7 @@ htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])), ...@@ -329,7 +305,7 @@ htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
``` ```
::: :::
## MXL: Case B with NR-index interaction ## MXL: Case B with NR-index Interaction
::: {style="font-size: 60%;"} ::: {style="font-size: 60%;"}
```{r, results='asis'} ```{r, results='asis'}
...@@ -350,7 +326,6 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])), ...@@ -350,7 +326,6 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
1. How do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences? 1. How do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences?
::: incremental ::: incremental
- Obligatory and voluntary treatments do not increase survey engagement measured via time spend on the survey - Obligatory and voluntary treatments do not increase survey engagement measured via time spend on the survey
- Small negative effect for obligatory treatment on survey engagement - Small negative effect for obligatory treatment on survey engagement
...@@ -360,21 +335,18 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])), ...@@ -360,21 +335,18 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
- No effect on consequentiality - No effect on consequentiality
- Strong effects on stated preferences for both treatments, more pronounced effect for the obligatory treatment - Strong effects on stated preferences for both treatments, more pronounced effect for the obligatory treatment
::: :::
## Discussion (2) ## Discussion (2)
2. Do socio-demographic variables or natural connectedness influence the decision to access voluntary information? 2. Do socio-demographic or attitudinal variables influence the decision to access voluntary information?
::: incremental ::: incremental
- Respondents that voluntary access information are younger, richer and have a higher natural relatedness index - Respondents that voluntary access information are younger, richer and have a higher natural relatedness index
- No effects of gender and education - No effects of gender and education
- Respondents' preferences for the good to be valued influence the likelihood of accessing additional information - Respondents' preferences for the good to be valued influence the likelihood of accessing additional information
::: :::
## Discussion (3) ## Discussion (3)
...@@ -382,19 +354,16 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])), ...@@ -382,19 +354,16 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
3. Do survey engagement, information recall, consequentiality, and stated preferences differ between respondents who access voluntary information and those who do not? 3. Do survey engagement, information recall, consequentiality, and stated preferences differ between respondents who access voluntary information and those who do not?
::: incremental ::: incremental
- Respondents that voluntary access information do engage more in the survey & have a higher consequentiality score - 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 - Voluntary information access is negatively correlated with number of status quo choices
- Higher willingness to pay values in groups that voluntary access information - Higher willingness to pay values in groups that voluntary access information
::: :::
## Conclusion ## Conclusion
::: incremental ::: incremental
- Obligatory and voluntary information treatments increase information recall and willingness to pay for naturalness of and proximity to urban green spaces - 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 - Exogenous treatments do not affect consequentiality
...@@ -406,39 +375,50 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])), ...@@ -406,39 +375,50 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
- Voluntarily accessed treatment shows strongest effects, but is highly endogenous - Voluntarily accessed treatment shows strongest effects, but is highly endogenous
- Providing optional information seem to rather increase inequality in good-specific knowledge than decreasing it - Providing optional information seem to rather increase inequality in good-specific knowledge than decreasing it
::: :::
## Appendix ## References
Information provision (Video) Link to the video: https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4
## Summary Statistics A
![](Grafics/sum_A.png){width="300"}
## Summary Statistics B
![](Grafics/sum_b_2.png){width="300"}
<!-- ## Socio Demografics {.smaller} -->
<!-- ::: {style="font-size: 50%;"} -->
<!-- ::: panel-tabset -->
<!-- ### Case A -->
<!-- ```{r} --> ::: {#refs}
<!-- kableExtra::kable(treatment_socio_A) --> :::
<!-- ``` -->
<!-- ### Case B --> # 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}
```
:::
<!-- ```{r} -->
<!-- kableExtra::kable(treatment_socio_C) -->
<!-- ``` -->
<!-- ::: -->
<!-- ::: -->
## NR OLS ## NR OLS
...@@ -472,4 +452,3 @@ ggplot(data=mxl_melt_info, aes(x=Coefficent, y=abs(value), fill=variable)) + ...@@ -472,4 +452,3 @@ ggplot(data=mxl_melt_info, aes(x=Coefficent, y=abs(value), fill=variable)) +
scale_fill_brewer(palette = "Set2", labels = c("Treated", "Optional Treatment", "Not Treated"), name="Treatment") + scale_fill_brewer(palette = "Set2", labels = c("Treated", "Optional Treatment", "Not Treated"), name="Treatment") +
theme(legend.position = c(0.85, 0.8)) theme(legend.position = c(0.85, 0.8))
``` ```
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