Stephanie Houle is a Senior Economist in the Canadian Economic Analysis Department. Her research interests include the Digital Economy, Firm Dynamics, applications of Machine Learning to Economics, and International Trade and Finance. Stephanie holds a PhD in Economics from McMaster University.
Since the mid-2000s, labour productivity has slowed down in Canada despite enormous technological advances that were expected to improve it. This note investigates whether mismeasurement of the digital economy can explain this paradox.
We develop high-frequency, news-based indicators using natural language processing methods to analyze news media texts. Our indicators track both supply (raw, intermediate and final goods) and labour shortages over time. They also provide weekly time-varying topic narratives about various types of shortages.
Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.