AI, Remote Work and Productivity: An International Conference

Chair: Rob Greenwood

Deputy Minister of Rural and Regional Development and Engagement

Government of Newfoundland and Labrador

Commentator: David Freshwater

Professor at University of Kentucky

Wulong Gu

Senior Advisor in the Economic Analysis Division at Statistics Canada

“AI, Innovation, and Productivity Growth in Canada”

Abstract:

This presentation explores AI’s potential as a general-purpose technology to drive innovation and productivity growth in Canada. It begins by analyzing micro-level evidence on AI adoption, revealing uneven uptake across firms and industries, along with its effects on product and process innovation. The discussion then connects AI to broader economic trends, including rising industry concentration and declining business dynamism, while assessing its implications for competition and market structure—key factors shaping AI’s aggregate productivity impact.

To quantify AI’s influence, the presentation introduces a source-of-growth accounting framework, decomposing labor productivity growth into three components: skill upgrading (via task automation/augmentation), capital deepening (investment in data/intangible assets), and multifactor productivity (MFP) growth (through technological diffusion and business dynamics). Finally, it benchmarks Canada’s performance in data, intangible assets, and MFP against the U.S. and Europe, offering insights into AI’s role in shaping cross-country productivity trends.

Mingwei Liu

Professor and Associate Dean for Research at the School of Management and Labor Relations, Rutgers University

“Automation, Occupational Structure, and Productivity: Evidence from Korean Workplace Panel Survey”

Abstract:

The impact of automation on productivity remains a subject of ongoing debate, with empirical studies yielding mixed results. Drawing on data from the 2019 and 2021 waves of the Korean Workplace Panel Survey (WPS), this study finds that automation has a positive effect on productivity. Our analysis highlights changes in occupational structure as a key mechanism linking automation to productivity gains. However, these gains are not evenly distributed: they vary significantly across industries and between unionized and non-unionized workplaces. Furthermore, the effect of automation is moderated by the intensity of worker training and the degree to which workers are involved in decision-making related to technology adoption.

Carl Lin 

Associate Professor of Economics, Bucknell University

“AI and Labor Market Outcomes: Evidence from China”

Abstract:

This paper examines the impact of Artificial Intelligence (AI) adoption on labor market outcomes in China, drawing on comprehensive individual-level household survey data from 2010 to 2022 and an extensive dataset of two million AI firms spanning 2010–2024, obtained from China’s National Enterprise Credit Information Publicity System. The study classifies firms as AI-related based on keywords in their registered business scope, including machine learning, big data analytics, cloud computing, data mining, virtual reality, natural language processing, robotics automation, smart home systems, voice recognition, deep learning, and autonomous driving. By merging these datasets, the analysis explores how the expansion of AI firms influences employment and wages. The paper places particular emphasis on regional heterogeneity, highlighting how variations in technological adoption and economic development across jurisdictions shape labor market outcomes. This research offers a novel empirical perspective on the labor market implications of AI diffusion in a rapidly evolving economy.