TrabahoLens — Philippine Job Market Visualizer

TrabahoLens is a research tool for exploring the Philippine labor market through an interactive, data-dense treemap. It visualizes 173 occupations across dimensions like employment size, wages, education requirements, and Generative AI (LLM) exposure.

Inspired by karpathy.ai/jobs, this adaptation is tailored for the Philippine context using data from the Philippine Statistics Authority (PSA) and O*NET.

🚀 Key Features

• Interactive Treemap: Explore the labor market at scale, where tile area represents employment volume.
• AI Exposure Scoring: Specialized rubric for measuring task reshaping risk from Large Language Models (LLMs).
• Economic Footprint: Real-time stats on total jobs, monthly payrolls, and informal sector prevalence.
• Bento Dashboard: A world-class, "morphic" UI with smooth transitions and glassmorphic details.
• PWA Ready: Installable as a mobile app with offline-capable metadata and theme support.
• Multi-Layer Analysis: Toggle between AI exposure, wages, education, OFW share, and hiring intensity.

🛠️ Quickstart

uv sync
uv run python scripts/serve.py

Then open http://127.0.0.1:8000.

📊 Common Commands

# Run code + data checks
uv run python scripts/check.py

# Rebuild site dataset (site/data.json)
uv run python scripts/build_site_data.py

# Optional: Rescore occupations with OpenRouter
# (Requires OPENROUTER_API_KEY in .env)
uv run python scripts/score_ai_only.py --model claude-3-5-haiku

Detailed scoring instructions can be found in docs/openrouter-setup.md.

🧪 Methodology

Data Sources

• PSA LFS & OWS 2024: Employment totals and wage reference material.
• PSA Survey on Overseas Filipinos: For calculating OFW share per occupational group.
• O*NET 30.2: For occupation descriptions and Job Zone (education) data.
• PhilJobNet & IBPAP: For real-time hiring intensity signals.

AI Exposure Scoring

We use an LLM-powered pipeline to score occupations on a 0–10 scale based on their potential for Generative AI task reshaping.

• Included: Task automation in coding, writing, analysis, and routine knowledge work.
• Excluded: Physical machinery automation, robotics, and platform-based dispatch algorithms.

📁 Data Layout

• data/reference/occupations_seed.json: Curated source occupation records.
• data/derived/occupations_ph.csv: Main derived occupation table.
• data/derived/scores_ai_only.json: AI exposure cache used by the site.
• site/data.json: Final built dataset for the frontend.

📜 Documentation

• CLAUDE.md: Development guide and code standards.
• AGENTS.md: AI scoring rubric and agent role descriptions.
• CHANGELOG.md: History of project updates.
• CONTRIBUTING.md: Guidelines for project contributors.
• SECURITY.md: Security reporting policy.