Stephen Hansen
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
Initial Match and Career Outcomes: Evidence from the NFL Draft
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.
PaperInference for Regression with Variables Generated from Unstructured Data
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.
PaperPolicymakers' Uncertainty
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.
PaperShort and Variable Lags
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 ColumnRemote Work across Jobs, Companies, and Space
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 WebsiteNational Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption
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.
PaperThe Demand for Executive Skills
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 ArticleFirm-level Risk Exposures and Stock Returns in the Wake of COVID-19
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