📅 2023-10-21 — Session: Developed regression models for tax morale analysis
🕒 17:30–19:40
🏷️ Labels: Tax Morale, Regression Models, Data Visualization, Python, Dataset Loading
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and refine regression models for analyzing tax morale, focusing on independent variables like public goods, community engagement, and political participation.
Key Activities
- Analyzed independent variables and methodologies in taxation research, highlighting statistical approaches.
- Outlined ordered logistic regression models, including interaction terms and city fixed effects.
- Refined statistical models for tax morale, incorporating controls such as satisfaction with tax revenue use and ethnic identity.
- Suggested regression models based on various independent variables related to public goods and community engagement.
- Executed Python code for data visualization, including integrating codebook information into histogram plotting and modifying code for better data visualization.
- Addressed errors in dataset loading and corrected file paths for proper data processing.
Achievements
- Developed a comprehensive framework for regression models to analyze tax morale.
- Enhanced data visualization techniques using Python.
- Resolved dataset loading issues and confirmed correct paths for data access.
Pending Tasks
- Further refine the regression models with additional independent variables.
- Continue improving data visualization techniques for clearer insights.