Developed regression models for tax morale analysis

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Developed regression models for tax morale analysis

  • Day: 2023-10-21
  • Time: 17:30 to 19:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Tax Morale, Regression Models, Data Visualization, Python, Dataset Loading

Description

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.

Evidence

  • source_file=2023-10-21.sessions.jsonl, line_number=1, event_count=0, session_id=485db0b673529e54f9368ce6eed4e83fee5b6dbeecbb95b8ed994d8bcb020825
  • event_ids: []

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