I write Parth's Datastack, data-driven economics for a general audience. A few I'm proud of:
Slop Detective: case notes from scanning a few thousand Substack posts with an AI detector, on who's publishing machine-written work and whether readers can tell.
Creative Destruction: unpacking the 2025 economics Nobel, and how fading competition and rising markups quietly throttle growth.
I've been writing about markets since university: finance and crypto columns for The Saint, St Andrews' student paper, as deputy editor (2021). One I've rescued from the archive: Boom or Bust?! The Bitcoin Bubble.
Projects
Council Receipts: what English councils talk about, next to where the money goes, matched to the same financial year. Built from meeting minutes via the Council Gateway API and MHCLG spending returns, each with a demographic and scorecard profile. Started at a Campaign Lab hack day and growing to cover all of England; code and data on GitHub, and the feedback tab lists what's next.
UK AI Economic Indicators: a live, interactive dashboard replicating the Stanford Digital Economy Lab's AI Economic Indicators for the UK. Three tracks, adoption, macroeconomic transformation, and the labour market, each built from public UK data (ONS, Bank of England, Ofcom, and Labour Force Survey microdata). Python and Plotly; the code and method are on GitHub.
Modelling Political Polarization: an interactive version of my St Andrews dissertation. A 5,000-agent simulation where you move sliders for exposure, tolerance, inequality, and elite influence, and watch a population polarise. Built in Python and Streamlit.
AI safety
While at St Andrews I co-founded and ran STAISH, the university's first AI safety fellowship: a multi-week reading group taking students through machine-learning fundamentals, AGI risk, alignment, and governance. The Saint covered the launch, and the reading syllabus is online.