Bio

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.

Research Field(s): Data Science Digital Economy Firm Dynamics International Trade and Finance Pricing Behaviour

Staff analytical notes

Overlooking the online world: Does mismeasurement of the digital economy explain the productivity slowdown?

Staff Analytical Note 2021-10 Alejandra Bellatin, Stephanie Houle
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.
Content Type(s): Staff research, Staff analytical notes Topic(s): Productivity JEL Code(s): E, E0, E01, L, L8, L86, O, O3, O33, O4, O5, O51

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Staff discussion papers

Turning Words into Numbers: Measuring News Media Coverage of Shortages

Staff Discussion Paper 2023-8 Lin Chen, Stephanie Houle
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.
Content Type(s): Staff research, Staff discussion papers Topic(s): Coronavirus disease (COVID-19), Econometric and statistical methods, Monetary policy and uncertainty, Recent economic and financial developments JEL Code(s): C, C5, C55, C8, C82, E, E3, E37

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Staff working papers

Identifying Nascent High-Growth Firms Using Machine Learning

Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald
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.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Firm dynamics JEL Code(s): C, C5, C55, C8, C81, L, L2, L25

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Journal publications

Published papers

  • "The Curious Incident of Luxury Imports during the Top-Income Surge," joint work with Michael Veall and Pau Pujolas. Economics Bulletin, AccessEcon, vol. 39(2), pages 1479-1487. 2019.