Tracking technical change: Past, present and future

Productivity growth in many countries has remained low for several years. Whether new technologies can reverse the trend depends on the scope of their impact and scale of their adoption—two dimensions of technical change that are historically difficult to measure. Here, we elaborate on the materials and methods presented in Alexopoulos's presidential address at the 2024 Canadian Economics Association meeting. Specifically, we discuss how applying natural language processing and text mining to library collections and cataloguing materials can help: (i) identify new technologies as they come to market and (ii) track their uses and spread over time. We further describe how our insights can be used to uncover general purpose technologies and macro-innovations in both the past and the present. An application to current data suggests that AI and robotics are responsible for an increasing share of recent technical change. Moreover, they resemble past early-stage general purpose technologies and thus do promise a reversal in productivity trends as their adoption increases. Going forward, our new methods should be especially useful to economists and policy-makers who need to track future development and adoption of key technologies—especially during periods of rapid innovation.