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Tracking technical change: Past, present and future

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  • Michelle Alexopoulos
  • Jon Cohen

Abstract

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. Suivi des changements technologiques : passé, présent et avenir. Dans de nombreux pays, la croissance de la productivité est restée faible pendant plusieurs années. Les nouvelles technologies peuvent‐elles inverser la tendance? Tout dépend de la portée de leur incidence et de l'ampleur de leur adoption, deux aspects du changement technologique qui sont historiquement difficiles à mesurer. Nous détaillons ici les méthodes et le matériel présentés dans le discours présidentiel de M. Alexopoulos lors de la rencontre annuelle 2024 de l'Association canadienne d'économique. Plus précisément, nous examinons l'utilité de l'application du traitement automatique du langage naturel (TALN) et de l'exploration de texte aux collections des bibliothèques et au catalogage des documents pour : (i) cerner les nouvelles technologies à mesure de leur mise en marché et (ii) suivre leur utilisation et leur adoption au fil du temps. Nous décrivons ensuite la façon d'utiliser nos constatations pour découvrir des technologies polyvalentes et des macro‐innovations dans le passé et le présent. Une application aux données actuelles suggère que l'IA et la robotique sont responsables d'une part croissante des changements technologiques récents. De plus, elles ressemblent aux premières technologies polyvalentes du passé et promettent donc un renversement des tendances en matière de productivité parallèlement à l'augmentation de leur adoption. À l'avenir, nos nouvelles méthodes devraient être particulièrement utiles aux économistes et aux décideurs politiques qui ont besoin de suivre l'évolution et l'adoption de technologies clés, surtout pendant les périodes d'innovation rapide.

Suggested Citation

  • Michelle Alexopoulos & Jon Cohen, 2024. "Tracking technical change: Past, present and future," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(4), pages 1047-1087, November.
    • Handle: RePEc:wly:canjec:v:57:y:2024:i:4:p:1047-1087
    DOI: 10.1111/caje.12749
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    References listed on IDEAS

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