Author: Briac Sockalingum
Purpose: Technical showcase for predoctoral research positions requiring SQL/database experience
Focus: Applied microeconomics, empirical analysis, data pipeline construction

This repository demonstrates SQL proficiency through economics research applications, progressing from foundational database operations to analytical queries for empirical research.

Python-SQL-showcase/
│
├── 01_fundamentals/              # Foundation: Basic SQL operations
│   ├── email_aggregation.py      # Text parsing, aggregation, frequency counting
│   ├── json_loader.py            # Relational schema design, normalization
│   ├── geoload.py                # API-to-database ETL, caching strategy
│   └── geodump.py                # Data export, JSON transformation
│
├── 02_research_pipelines/        # Advanced: Research-grade applications
│   ├── panel_constructor.py      # Panel data construction, DiD preparation
│   ├── advanced_queries.py       # Window functions, optimization techniques
│   └── api_etl_pipeline.py       # FRED API integration, macro data merging
│
├── 03_sample_data/
│   └── economic_research.db      # SQLite database with firm panel data
│
├── .gitignore
└── README.md

economic_research.db contains:

• 500 firms across 10 states (5 treatment, 5 control)
• 5 industries with varying labor intensity
• 20 quarters of data (2018Q1 - 2022Q4)
• 10,000 firm-quarter observations

Variables:

• Firm characteristics: industry, state, founding year
• Outcomes: employment, revenue, wage bill, profits
• Treatment: binary indicator for states raising minimum wage

Developed as part of predoctoral application portfolio demonstrating SQL proficiency for data-intensive research positions.

Learning trajectory:
Foundational course (py4e) → Self-study of SQL → This portfolio

• Foundational SQL exercises inspired by Python for Everybody (py4e) by Dr. Charles Severance
• Sample economic data structures based on common empirical microeconomics research designs
• FRED API integration pattern follows Federal Reserve API documentation