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.)
Initial Match and Career Outcomes: Evidence from the NFL Draft
with Pablo Casas-Arce, Miguel Martinez-Carrasco, and Asis Martinez-Jerez

NFL draft rules randomly allocate players of similar quality to teams of different quality. But there is no effect of initial match on long-term performance, suggesting that firms do not contribute to long-term inequality in sufficiently competitive markets.

Paper
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.

Paper
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.

Paper
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
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.

Paper
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