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stratification-mobility

Estimate intergenerational income and occupational mobility from panel data.

33|6|Updated Mar 17, 2026
Search Tags:#stratification#intergenerational mobility#rank-rank slope#occupational prestige#transition matrix#inequality metrics
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill stratification-mobility
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stratification-mobility
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/10-sociology/stratification-mobility
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill stratification-mobility

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas>=1.5, statsmodels>=0.14, numpy>=1.23, matplotlib>=3.6, scipy>=1.9.

What problem does it solve?

Social stratification analysis often requires estimating how strongly family background predicts adult outcomes and how people move across occupations and class positions across generations.

Core Features & Use Cases

  • Estimate intergenerational income mobility: compute intergenerational income elasticity (IGE) using log-log regression and estimate the rank-rank slope (Chetty rank-rank) with bootstrap confidence intervals.
  • Measure mobility outcomes: calculate upward mobility rates such as P(top quintile | bottom quintile), and analyze occupational prestige using ISEI scoring.
  • Build occupational transition evidence: assign EGP class labels, compute origin-to-destination transition matrices, and derive social fluidity odds ratios from contingency tables.

Quick Start

Use the stratification-mobility skill to estimate IGE, rank-rank slope with a bootstrap confidence interval, and an upward mobility rate from your parent_income and child_income columns.

Frequently Asked Questions about stratification-mobility

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

FAQPage Schema
How do I estimate intergenerational income elasticity using panel survey data?▼

You can estimate intergenerational income elasticity (IGE) by running a log-log OLS regression on parent and child income columns. The skill provides deterministic, function-based workflows to calculate this elasticity directly from your pandas DataFrame.

Can I calculate Chetty-style rank-rank slope with bootstrap confidence intervals in pandas?▼

Yes, you can calculate the Chetty-style rank-rank slope with bootstrap confidence intervals. The skill uses statsmodels and scipy to estimate the slope and generate robust confidence intervals from parent and child income arrays.

What is the best way to build an occupational transition matrix from EGP class labels?▼

The best way to build an occupational transition matrix is to assign EGP class labels to your origin and destination columns, then compute a normalized crosstab matrix. The skill handles this transition-matrix construction and derives social fluidity odds ratios from the contingency tables.

Does this skill support ISEI prestige scoring and upward mobility rate calculation?▼

Yes, the skill supports ISEI prestige scoring to analyze occupational prestige. It also calculates upward mobility rates, such as the probability of reaching the top quintile from the bottom quintile, using numpy for data handling.

Do I need specific Python library versions to run intergenerational mobility metrics?▼

Yes, you need specific library versions including pandas 1.5 or higher, statsmodels 0.14 or higher, and scipy 1.9 or higher. These dependencies are required for statistical estimation and deterministic data workflows.

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