Digitalization and productivity

• COVID-19 and Implications for Automation

  Staff working paper 2021-25 Alex Chernoff, Casey Warman

  Occupations held by females with mid-level education face the highest risk of accelerated automation as a result of the COVID-19 pandemic.

  Content Type(s): Staff research, Staff working papers JEL Code(s): I, I1, I14, I2, I24, J, J1, J15, J16, R, R1, R12 Research Theme(s): Monetary policy, Real economy and forecasting, Structural challenges, Demographics and labour supply, Digitalization and productivity

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

  Staff analytical note 2021-10 Alejandra Bellatin, Stéphanie 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 JEL Code(s): E, E0, E01, L, L8, L86, O, O3, O33, O4, O5, O51 Research Theme(s): Models and tools, Economic models, Structural challenges, Digitalization and productivity

• Adoption of Digital Technologies: Insights from a Global Survey Initiative

  Staff discussion paper 2021-7 James Fudurich, Lena Suchanek, Lise Pichette

  Firms are at the forefront of adopting new technology. Using survey data from a global network of central banks, we assess the effects of digitalization on firms’ pricing and employment decisions.

  Content Type(s): Staff research, Staff discussion papers JEL Code(s): D, D2, D22, E, E3, E31, J, J2, J21, O, O3, O33 Research Theme(s): Monetary policy, Inflation dynamics and pressures, Structural challenges, Demographics and labour supply, Digitalization and productivity

• Allocative Efficiency and the Productivity Slowdown

  Staff working paper 2021-1 Lin Shao, Rongsheng Tang

  In our analysis of the US productivity slowdown in the 1970s and 2000s, we find that a significant portion of this deceleration can be attributed to a lack of improvement in allocative efficiency across sectors. Our analysis further identifies increased sector-level volatility as a major contributor to this lack of improvement in allocative efficiency.

  Content Type(s): Staff research, Staff working papers JEL Code(s): E, E2, E23, O, O4, O47 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting, Structural challenges, Digitalization and productivity

• Assessing Global Potential Output Growth: October 2020

  Staff discussion paper 2020-10 Xin Scott Chen, Ali Jaffery, Guillaume Nolin, Karim Salhab, Peter Shannon, Subrata Sarker

  This paper presents updated estimates of potential output growth for the global economy through 2022. Global potential output growth is expected to decline sharply in the aftermath of the COVID-19 pandemic and recover partially by the end of the projection horizon of the October 2020 Monetary Policy Report.

  Content Type(s): Staff research, Staff discussion papers JEL Code(s): E, E1, E10, E2, E20, O, O4 Research Theme(s): Monetary policy, Real economy and forecasting, Structural challenges, Demographics and labour supply, Digitalization and productivity

• Potential output in Canada: 2020 reassessment

  Staff analytical note 2020-25 Dany Brouillette, Julien Champagne, Julien McDonald-Guimond

  After COVID-19, we expect potential output growth to stabilize around 1.2 percent. This is lower than the 2010–18 average growth of 1.8 percent. Relative to the April 2019 reassessment, the growth profile is revised down. Given the unknown course of the pandemic, uncertainty around these estimates is higher than in previous years.

  Content Type(s): Staff research, Staff analytical notes JEL Code(s): E, E0, E00, E2, E23, E24, E3, E37, E6 Research Theme(s): Monetary policy, Real economy and forecasting, Structural challenges, Demographics and labour supply, Digitalization and productivity

• Technology Adoption in Input-Output Networks

  Staff working paper 2019-51 Xintong Han, Lei Xu

  We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs.

  Content Type(s): Staff research, Staff working papers JEL Code(s): C, C6, C61, L, L2, L23, L8, L86, O, O1, O14, O3, O33 Research Theme(s): Models and tools, Economic models, Structural challenges, Digitalization and productivity

• Changing Fortunes: Long-Termism—G-Zero, Artificial Intelligence and Debt

  Staff discussion paper 2019-12 Stephen S. Poloz

  This paper discusses three long-term forces that are acting on the global economy and their implications for companies and policy-makers.

  Content Type(s): Staff research, Staff discussion papers JEL Code(s): E, E6, E63, F, F0, F02, F1, F15, F5, F53, F6, H, O, O1, O11, O3, O33 Research Theme(s): Financial system, Financial stability and systemic risk, Structural challenges, Demographics and labour supply, Digitalization and productivity, International trade, finance and competitiveness

• Technological Progress and Monetary Policy: Managing the Fourth Industrial Revolution

  Staff discussion paper 2019-11 Stephen S. Poloz

  This paper looks at the implications for monetary policy of the widespread adoption of artificial intelligence and machine learning, which is sometimes called the “fourth industrial revolution.”

  Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C5, E, E3, O, O1, O11, O3, O33 Research Theme(s): Financial system, Financial stability and systemic risk, Models and tools, Economic models, Monetary policy, Monetary policy framework and transmission, Structural challenges, Digitalization and productivity

• Amazon Effects in Canadian Online Retail Firm-Product-Level Data

  Staff working paper 2019-42 Alex Chernoff

  I use firm-product-level data for Canadian online retailers to study how product scope (the average number of product categories per firm) evolved from 1999 to 2012. During this period, product scope dropped monotonically from 59 to 5 product categories.

  Content Type(s): Staff research, Staff working papers JEL Code(s): D, D2, D22, L, L1, L11, L8, L81 Research Theme(s): Structural challenges, Digitalization and productivity, International trade, finance and competitiveness