Applied Economist · Data Scientist

Michael Early

Applied economist and data scientist. My research uses causal inference and quasi-experimental methods to study labor market policy — most recently, the employment effects of California's fast-food wage law. MA in Economics, Cal State Fullerton. Currently working as an ITSM Process Manager at First American.

View research Resume ↓
Michael Early
Michael Early
MA Economics, CSUF · 2026
ITSM Process Manager · First American

I'm a quantitative economist with an MA in Economics from Cal State Fullerton (GPA 3.93), concentrating in applied econometrics and labor economics. Most of my work sits at the intersection of economic policy and data, and I care a lot about research design — getting the causal question right before worrying about the model.

I work as an ITSM Process Manager in Enterprise Architecture at First American. During my graduate program I interned on the same team, building GPT-powered tools, data pipelines, and analytical dashboards in Power BI, SQL, and Databricks. At CSUF I also taught and assisted in the Economics Department.

I am based in Southern California.

Interests

  • Applied microeconometrics
  • Labor economics
  • ML + causal inference

Education

  • MA in Economics, 2026 California State University, Fullerton
  • BA in Economics, 2024 California State University, Fullerton

Featured Research

MA Thesis · 2026
Sector-Specific Wage Floors and Young Adult Labor Market Outcomes: Evidence from California's AB 1228
Michael Early · MA Thesis · California State University, Fullerton · 2026
Committee: Maria Casanova (chair), Pedro Amaral, Kristin Kleinjans

CPS + QCEW evidence on the first statewide sector-specific wage floor in US history. All-state TWFE, synthetic control, and contiguous-county border designs. The aggregate youth employment effect is small and not robust; adjustment runs through the intensive margin (wages up 2.1%, hours down 3.8%); and the paper's central contribution is compositional — workers without a high school diploma or with only a high school diploma see ~3.2 pp employment declines while those with some college see ~2.4 pp gains.

Published paper ↗ Case study →

Experience

ITSM Process Manager
First American · Enterprise Architecture · Santa Ana, CA
Jun 2026 – Present
Graduate Intern — Enterprise Architecture
First American · Santa Ana, CA
Jun 2025 – Jun 2026
  • Designed and deployed a production GPT-powered Q&A assistant over 200+ enterprise architecture documents, eliminating manual lookup workflows and enabling self-serve query resolution for internal engineering teams — reducing analyst overhead by ~3–4 hours per week
  • Built an agentic dependency-mapping pipeline that auto-extracts and graphs application relationships during system onboarding, surfacing architectural risk and creating the first standardized process for capturing cross-enterprise dependencies where none previously existed
  • Engineered reproducible SQL reporting pipelines over large-scale operational datasets, replacing ad hoc queries with auditable scheduled reports — reducing manual reconciliation time and enabling end-to-end auditability for quarterly performance reviews
  • Automated analytical reporting workflows enforcing methodological consistency across data products, improving reliability and transparency for cross-team stakeholders and senior architecture leadership
  • Presented enterprise-wide business architecture standings to executive leadership as part of the internship program, translating complex system-level findings into structured briefings for senior decision-makers
Instructional Student Assistant
CSUF Economics Department · Fullerton, CA
Jan 2025 – May 2026
  • Supported undergraduates in Intermediate Business Microeconomics through weekly office hours (homework, exam prep, concept review); proctored and graded exams across a 200-student course
Teaching Assistant — Principles of Microeconomics
CSUF Economics Department · Fullerton, CA
Jan 2025 – Aug 2025
  • Taught weekly discussion sections for 40 undergraduates in Principles of Microeconomics; graded assignments and exams for a 120-student course
Graduate Assistant — Institutional Research
CSUF Economics Department · Fullerton, CA
Jan 2025 – May 2025
  • Gathered, cleaned, and performed preliminary analysis on institutional enrollment data sourced from IPEDS; structured outputs to support departmental reporting and planning

Projects

Research · ML · Sports analytics
MLB Biomechanics Velocity Study
Leakage-safe OpenBiomechanics pitching model with repeated grouped validation, feature-set comparisons, metric ablations, high-velocity residual diagnostics, and a separate Statcast pitch-trait bridge
GitHub repo →
Sports ML
NFL Game Prediction System
Reusable NFL prediction pipeline featuring Eagles 2026 forecasts, leakage-aware feature engineering, season-forward validation, calibration analysis, and a Streamlit dashboard
ML System
NBA Playoff Series Predictor
Series-level playoff model using pre-series regular-season features, temporal validation, XGBoost, SHAP explainability, and generated bracket probability visuals
GitHub repo →
Sports ML
Fast-Food Minimum Wage (AB 1228)
All-state TWFE on CPS microdata, triangulated with synthetic control and QCEW border-county evidence — small aggregate effect with compositional reallocation across education groups
Causal Inference
NFL Quarterback Wage Discrimination
Blinder-Oaxaca decomposition — racial wage gap disappears after controlling for on-field performance
Labor Economics
Forecasting Super Bowl Odds
Time-series models and efficiency tests on Super Bowl futures — market prices are informationally efficient
Sports Analytics
NFL Sunday Ticket Pricing Model
IO model of monopoly bundling — optimal pricing under exclusive distribution in broadcasting markets
Industrial Org
Housing Price Prediction
Gradient boosting with feature engineering — ensemble methods outperform OLS on held-out test set
Machine Learning
Economic Growth ML Study
Collaborative macroeconomic analysis using World Bank indicators, HDI data, k-means clustering, Random Forest regression, and feature-importance interpretation
Applied Econ

Technical Skills

Languages
Python SQL R Stata C++
Econometrics
DiD IV RD Panel methods Causal inference
ML & AI
Scikit-learn XGBoost SHAP Hugging Face PyTorch OpenAI API
Data & Viz
Pandas NumPy Matplotlib Power BI Databricks Streamlit

Contact

Email — michaelarly.mpe@gmail.com
LinkedIn — linkedin.com/in/michael-early-
GitHub — github.com/michael-early