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