Global Migration & Labor Integration Analysis

Project Overview

This project provides a comprehensive analytical framework for evaluating global migration trends and labor market integration using multi-dimensional OECD datasets[cite: 31]. It automates the ingestion, cleaning, and visualization of international labor mobility data to quantify parity across demographic cohorts.

The system utilizes a modular Python architecture to transform raw JSON API responses into actionable insights and structured exports.

Modular Architecture

The repository is structured to ensure scalability and reproducibility:

• data_loader.py: A specialized module for automating the ingestion of multi-dimensional OECD datasets. It handles API requests, standardizes ISCED education levels, and cleanses migration categories across disparate international sources.
• analysis_functions.py: Contains the core logic for calculating integration KPIs, such as native vs. foreign-born employment gaps.
• vedh_jaishankar_study6.ipynb: The primary research notebook that orchestrates the workflow, performs exploratory data analysis (EDA), and generates time-series visualizations.

Technical Stack

• Language: Python
• Libraries: Pandas, Seaborn, Matplotlib
• API: OECD Data API (JSON)
• Methodology: Descriptive Statistics, Time-Series Analysis, KPI Engineering

Getting Started

1. Requirements: Install dependencies via pip install pandas seaborn matplotlib requests.
2. Execution: Run the vedh_jaishankar_study6.ipynb notebook to initiate the full data pipeline from extraction to visualization.
3. Data Exports: The workflow automatically processes raw responses into structured CSV exports for external stakeholder analysis.