Energy Economics • AI Infrastructure • Applied Econometrics
I'm Alan Lamb, an applied economist focused on energy markets and AI infrastructure. I'm completing an M.S. in Applied Economics at the University of Maryland. This site collects my research, writing, and technical work — papers, causal analyses, and software I've built. The reasoning is visible, the code is public, and the limitations are documented.
Between 2019 and 2025, electricity demand in ERCOT grew 27 percent while PJM and MISO were essentially flat. This paper asks how much of that divergence data center investment can explain. Using a four-method identification strategy — panel regression, synthetic control, difference-in-differences, and narrative validation — I estimate a 34.8 index-point divergence in ERCOT minimum hourly demand above a synthetic counterfactual. The paper also documents the absence of public load-side queue data as a policy finding.
Read on SSRN • GitHub • Interactive Dashboard
March 2026 • University of Maryland • Synthetic Control • DiD • Panel Regression • ARIMA • XGBoost
A mobile-first daily intelligence tool I designed and built solo. Bearing delivers a personalized morning brief that connects your work, reading, and projects to what's happening in the world. It runs on React, Supabase, and the Claude API, with Stripe for payments and Vercel for deployment — built from zero to fully deployed with auth, AI, and cross-device sync.
2026 • React • Vite • Supabase • Claude API • Stripe • Vercel