Identifying Nascent High-Growth Firms Using Machine Learning Staff working paper 2023-53 Stéphanie 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 JEL Code(s): C, C5, C55, C8, C81, L, L2, L25 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Financial Constraint and Productivity: Evidence from Canadian SMEs Staff working paper 2016-44 Shutao Cao, Danny Leung The degree to which financial constraint is binding is often not directly observable in commonly used business data sets (e.g., Compustat). In this paper, we measure and estimate the likelihood of a firm being constrained by external financing using a data set of small- and medium-sized Canadian firms. Content Type(s): Staff research, Staff working papers JEL Code(s): D, D2, D24, G, G3, G32, L, L2, L25 Research Theme(s): Financial system, Financial institutions and intermediation, Household and business credit, Models and tools, Economic models, Structural challenges, Digitalization and productivity