Stephen Hansen

Stephen Hansen Photo

About Me

I am a Professor of Economics at University College London. My current research uses unstructured data to build new measures of economic activity and behavior across a variety of applications, most often related to organizational economics and monetary policy. I maintain a Github page where I share code and lecture slides related to methodologies for the analysis of unstructured and high-dimensional data. I co-organize the monthly AMLEDS webinar that explores economic applications of machine learning. My research is supported by an ERC Conslidator Grant.

Current Working Papers

(Hover on a paper to expand. On mobile, tap to expand.)
Inference for Regression with Variables Generated from Unstructured Data
with Laura Battaglia, Tim Christensen, and Szymon Sacher

Plugging measures derived from unstructured data into regression models leads to biased inference. Our proposed solution is to jointly model informational retrieval and regression, which modern computational methods make possible.

Policymakers' Uncertainty
with Anna Cieslak, Michael McMahon, and Song Xiao

Inflation uncertainty leads to more hawkish policy stances on the Federal Open Market Committee. This appears to arise from concerns for tail risk than model parameter uncertainty.

Short and Variable Lags
with Gergely Buda, Vasco Carvalho, Giancarlo Corsetti, Joao Duarte, Afonso Moura, Alvaro Ortiz, Tomasa Rodrigo, Sevi Rodriguez Mora, and Guilherme Alves da Silva

Using novel daily measures of consumption, firm sales, and employment, we show that the economy reacts within days to monetary policy shocks. Time aggregation of the measures to quarterly frequency masks these short-run dynamics.

Paper VoxEU Column
Remote Work across Jobs, Companies, and Space
with Peter Lambert, Nick Bloom, Steven Davis, Raffaella Sadun, and Bledi Taska

We use a large language model trained on tens of thousands of human labels to measure remote and hybrid work adoption with unprecedented granularity. We document large heterogeneity in adoption across narrow occupation categories, cities, and firms.

Paper VoxEU Column Website
Graphical Model Inference With External Network Data
with Jack Jewson, Li Li, Laura Battaglia, David Rossell, and Piotr Zweirnik

We show how observed connections among variables in multiple networks can improve estimation of the precision matrix by targeting regularization and develop a Bayesian model for inferring pairwise correlations.

Paper GitHub Repository
National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption
with Gergely Buda, Vasco Carvalho, Alvaro Ortiz, Tomasa Rodrigo, and Sevi Rodriguez Mora

We use comprehensive financial transactions from one of Europe's largest banks to build a large-scale consumption survey using national accounting principles. We use this to build aggregate and distributional accounts for consumption, as well as a detailed individual consumption panel.

The Demand for Executive Skills
with Tejas Ramdas, Raffaella Sadun, and Joseph Fuller

We measure the demand for executive skills aross firms using a unique, large-scale corpus of job specifications. The importance of social skills is growing over time and is related to firm size, firm scope, and the information intensity of worker skills.

Paper HBR Article
Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19
with Steven Davis and Cristhian Seminario-Amez

We combine elements of supervised machine learning and dictionary methods to uncover narrow, interpretable risk exposures from 10-K filings that account for firm-level reactions to aggregate shocks. We apply this idea to study equity returns during the COVID-19 pandemic.

Paper VoxEU Column