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.