Working Paper: NBER ID: w29767
Authors: Avi Goldfarb; Bledi Taska; Florenta Teodoridis
Abstract: General purpose technologies (GPTs) push out the production possibility frontier and are of strategic importance to managers and policymakers. While theoretical models that explain the characteristics, benefits, and approaches to create and capture value from GPTs have advanced significantly, empirical methods to identify GPTs are lagging. The handful of available attempts are typically context specific and rely on hindsight. For managers deciding on technology strategy, it means that the classification, when available, comes too late. We propose a more universal approach of assessing the GPT likelihood of emerging technologies using data from online job postings. We benchmark our approach against prevailing empirical GPT methods that exploit patent data and provide an application on a set of emerging technologies. Our application exercise suggests that a cluster of technologies comprised of machine learning and related data science technologies is relatively likely to be GPT.
Keywords: General Purpose Technologies; Machine Learning; Emerging Technologies; Job Postings
JEL Codes: O32; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| machine learning (ML) (C45) | classified as GPTs (P30) |
| job posting data (J60) | predict future changes in GPT likelihood (C53) |
| absence of systematic methodology (B41) | missed opportunities for organizations (L25) |
| job posting-based measures (J68) | rank technologies like ML, big data highly on GPT criteria (C55) |
| technologies like ML, big data (C55) | drive substantial economic impacts (F69) |