vdem-analysis

Analyze V-Dem panel data to detect backsliding and run panel regressions.

33|6|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill vdem-analysis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vdem-analysis
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/09-political-science/vdem-analysis
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill vdem-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, linearmodels, scipy, statsmodels, requests.

What problem does it solve?

This Skill turns large V-Dem democracy datasets into measurable democratic indices, identifies democratic backsliding episodes, and enables panel regression analyses linking institutional quality to economic outcomes.

Core Features & Use Cases

  • V-Dem data loading at scale: Efficiently loads the V-Dem Country-Year CSV using selective columns and chunked reading, with optional year and country filtering.
  • Backsliding detection: Flags country-years where a democracy index declines by a configurable threshold over a rolling window and ranks episodes by severity.
  • Publication-ready visualization: Produces democracy trend plots for selected countries and regional comparison plots (e.g., distribution by region for a given year).
  • Panel regression pipeline: Runs Fixed Effects, Random Effects, or Pooled OLS models with clustered standard errors and optional log transformation of key predictors (e.g., GDP per capita).
  • Use case: Assess whether within-country changes in economic development (GDP per capita) are associated with changes in liberal democracy over time while also auditing where and when backsliding occurs.

Quick Start

Use the V-Dem Country-Year Core CSV to compute backsliding episodes and run a fixed-effects panel regression of liberal democracy on (log) GDP per capita for your chosen year range and countries.

Frequently Asked Questions about vdem-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect democratic backsliding episodes using V-Dem panel data?

To detect democratic backsliding episodes in V-Dem panel data, you flag country-years where a democracy index declines by a configurable threshold over a rolling lag window, then rank episodes by severity.

How do I run a fixed effects panel regression on V-Dem country-year data?

You can run fixed effects panel regression on V-Dem country-year data by using panel econometric estimators with clustered standard errors, applying optional log transformations to predictors like GDP per capita.

Can I analyze large V-Dem datasets with pandas without memory issues?

Yes, you can analyze large V-Dem datasets with pandas by using chunked CSV loading and selecting specific V-Dem columns, with optional year and country filtering to manage memory efficiently.

What is the best way to visualize democracy index trends for comparative politics?

The best way to visualize democracy index trends for comparative politics is generating publication-ready plots showing democracy trends for selected countries and regional comparison plots for a given year.

Does this panel regression approach support log-transformed GDP per capita predictors?

Yes, this panel regression approach supports log-transformed GDP per capita predictors, allowing you to estimate how within-country changes in economic development relate to liberal democracy over time.

What types of panel econometric estimators can I use for institutional quality analysis?

For institutional quality analysis, you can use Fixed Effects, Random Effects, or Pooled OLS panel econometric estimators with clustered standard errors to test institutional changes.